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• Fractional Polya Integral Inequality.- Univariate Fractional Polya Integral Inequalities.- About Multivariate General Fractional Polya Integral Inequalities.- Balanced Canavati Fractional Opial Inequalities.- Fractional Representation Formulae and Fractional Ostrowski Inequalities.- Basic Fractional Integral Inequalities.- Harmonic Multivariate Ostrowski and Gruss Inequalities.- Fractional Ostrowski and Gruss Inequalities Using Several Functions.- Further Interpretation of Some Fractional Ostrowski and Gruss Type Inequalities.- Multivariate Fractional Representation Formula and Ostrowski Inequality.- Fractional Representation Formulae and Ostrowski Inequalities.- About Multivariate Lyapunov Inequalities.- Ostrowski Type Inequalities for Semigroups.- About Ostrowski Inequalities for Cosine and Sine Operator Functions.- About Hilbert-Pachpatte Inequalities.- About Ostrowski and Landau Type Inequalities.- Multidimensional Ostrowski Type Inequalities.- About Fractional Representation Formulae and Right Fractional Inequalities.- About Canavati fractional Ostrowski inequalities.- The Most General Fractional Representation Formula.- Rational Inequalities for Integral Operators Using Convexity.- Fractional Integral Inequalities with Convexity.- Vectorial Inequalities for Integral Operators.- Vectorial Splitting Rational Lp Inequalities for Integral Operators.- Separating Rational Lp Inequalities for Integral Operators.- About Vectorial Hardy Type Fractional Inequalities.- About Vectorial Fractional Integral Inequalities Using Convexity.
• (source: Nielsen Book Data)

This monograph presents recent and original work of the author on inequalities in real, functional and fractional analysis. The chapters are self-contained and can be read independently, they include an extensive list of references per chapter. The book's results are expected to find applications in many areas of applied and pure mathematics, especially in ordinary and partial differential equations and fractional differential equations. As such this monograph is suitable for researchers, graduate students, and seminars of the above subjects, as well as Science and Engineering University libraries.
(source: Nielsen Book Data)

• A strong left Fractional Calculus for Banach space valued functions.- Strong Right Abstract Fractional Calculus.- Strong mixed and generalized Abstract Fractional Calculus.- Foundations of General Fractional Analysis for Banach space valued functions.- Vector abstract fractional Korovkin Approximation.- Basic Abstract Korovkin theory.- High Approximation for Banach space valued functions.- Vectorial abstract fractional approximation using linear operators.- Abstract fractional trigonometric Korovkin approximation.- Multivariate Abstract Approximation for Banach space valued functions.- Arctangent function based Abstract Neural Network approximation. .
• (source: Nielsen Book Data)

This brief book presents the strong fractional analysis of Banach space valued functions of a real domain. The book's results are abstract in nature: analytic inequalities, Korovkin approximation of functions and neural network approximation. The chapters are self-contained and can be read independently. This concise book is suitable for use in related graduate classes and many research projects. An extensive list of references is provided for each chapter. The book's results are relevant for many areas of pure and applied mathematics. As such, it offers a unique resource for researchers, and a valuable addition to all science and engineering libraries.
(source: Nielsen Book Data)

• Newton-Like Methods on Generalized Banach Spaces and Fractional Calculus
• Semilocal Convegence of Newton-Like Methods and Fractional Calculus
• Convergence of Iterative Methods and Generalized Fractional Calculus
• Fixed Point Techniques And Generalized Right Fractional Calculus
• Approximating Fixed Points And K-Fractional Calculus
• Iterative Methods And Generalized G-Fractional Calculus
• Unified Convergence Analysis For Iterative Algorithms And Fractional Calculus
• Convergence Analysis For Extended Iterative Algorithms And Fractional And Vector Calculus
• Convergence Analysis For Extended Iterative Algorithms And Fractional Calculus
• Secant-Like Methods And Fractional Calculus
• Secant-Like Methods And Modified G- Fractional Calculus
• Secant-Like Algorithms And Generalized Fractional Calculus
• Secant-Like Methods And Generalized G-Fractional Calculus Of Canavati-Type
• Iterative Algorithms And Left-Right Caputo Fractional Derivatives
• Iterative Methods On Banach Spaces With A Convergence Structure And Fractional Calculus
• Inexact Gauss-Newton Method For Singular Equations
• The Asymptotic Mesh Independence Principle
• Ball Convergence Of A Sixth Order Iterative Method
• Broyden's Method With Regularily Continuous Divided Differences
• Left General Fractional Monotone Approximation
• Right General Fractional Monotone Approximation Theor
• Left Generalized High Order Fractional Monotone Approximation
• Right Generalized High Order Fractional Monotone Approximation
• Advanced Fractional Taylor's Formulae
• Generalized Canavati Type Fractional Taylor's Formulae.

In this monograph the authors present Newton-type, Newton-like and other numerical methods, which involve fractional derivatives and fractional integral operators, for the first time studied in the literature. All for the purpose to solve numerically equations whose associated functions can be also non-differentiable in the ordinary sense. That is among others extending the classical Newton method theory which requires usual differentiability of function. Chapters are self-contained and can be read independently and several advanced courses can be taught out of this book. An extensive list of references is given per chapter. The book's results are expected to find applications in many areas of applied mathematics, stochastics, computer science and engineering. As such this monograph is suitable for researchers, graduate students, and seminars of the above subjects, also to be in all science and engineering libraries.

• Fixed Point Results and Applications in Left Multivariate Fractional Calculus
• Fixed Point Results and Applications in Right Multivariate Fractional Calculus
• Semi-local Iterative Procedures and Applications In K-Multivariate Fractional Calculus
• Newton-like Procedures and Applications in Multivariate Fractional Calculus
• Implicit Iterative Algorithms and Applications in Multivariate Calculus
• Monotone Iterative Schemes and Applications in Fractional Calculus
• Extending the Convergence Domain of Newton?s Method
• The Left Multidimensional Riemann-Liouville Fractional Integral
• The Right Multidimensional Riemann-Liouville Fractional Integral.

In this short monograph Newton-like and other similar numerical methods with applications to solving multivariate equations are developed, which involve Caputo type fractional mixed partial derivatives and multivariate fractional Riemann-Liouville integral operators. These are studied for the first time in the literature. The chapters are self-contained and can be read independently. An extensive list of references is given per chapter. The book?s results are expected to find applications in many areas of applied mathematics, stochastics, computer science and engineering. As such this short monograph is suitable for researchers, graduate students, to be used in graduate classes and seminars of the above subjects, also to be in all science and engineering libraries.

• Rate of Convergence of Basic Neural Network Operators to the Unit.- Rate of Convergence of Basic Multivariate Neural Network Operators.- Fractional Neural Network Operators Approximation.- Fractional Approximation Using Cardaliaguet-Euvrard Neural Networks.- Fractional Asymptotic Expansions for Quasi-interpolation neural Networks.- Voronovskaya Type Asymptotic Expansions for Multivariate Neural Networks.- Fractional Approximation by Bell and Squashing Neural Networks.- Fractional Asymptotic Expansions For Bell And Squashing Neural Networks.- Multivariate Asymptotic Expansions for Bell and Squashing Neural Networks.- Multivariate Fuzzy-Random Normalized Neural Network Approximation.- Fuzzy Fractional Approximations by Fuzzy Bell and Squashing Neural Networks.- Fuzzy Fractional Neural Network Approximation.- Multivariate Fuzzy Approximation Using Basic Neural Network Operators.- Multivariate Fuzzy Approximation Using Quasi-Interpolation Neural Networks.- Multivariate Fuzzy-Random Neural Networks Approximation.- Approximation by Kantorovich and Quadrature type neural Networks.- Univariate Error Function Based Neural Network Approximations.- Multivariate Error Function Based Neural Network Operators Approximation.- Asymptotic Expansions for Error Function Based Neural Networks.- Fuzzy Fractional Error Function Relied Neural Network Approximations.- Multivariate Fuzzy Approximation by Neural Networks.- Fuzzy-Random Error Function Relied Neural Network Approximations.- Approximation by Perturbed Neural Networks.- Approximations by Multivariate Perturbed Neural Networks.- Voronovskaya type Asymptotic Expansions for Perturbed Neural Networks.- Approximation using Fuzzy Perturbed Neural Networks.- Multivariate Fuzzy Perturbed Neural Network Approximations.- Multivariate Fuzzy-Random Perturbed Neural Network Approximations.
• (source: Nielsen Book Data)

This monograph is the continuation and completion of the monograph, "Intelligent Systems: Approximation by Artificial Neural Networks" written by the same author and published 2011 by Springer. The book you hold in hand presents the complete recent and original work of the author in approximation by neural networks. Chapters are written in a self-contained style and can be read independently. Advanced courses and seminars can be taught out of this brief book. All necessary background and motivations are given per chapter. A related list of references is given also per chapter. The book's results are expected to find applications in many areas of applied mathematics, computer science and engineering. As such this monograph is suitable for researchers, graduate students, and seminars of the above subjects, also for all science and engineering libraries.
(source: Nielsen Book Data)

• Prologue 7 1. Introduction 10 1.1 Autonomous Systems 13 1.2 The Role of Machine Learning in Autonomous Systems 15 1.3 System Identification 20 1.3.3 Novelty detection, outliers and the link to structure innovation 20 1.4 On-line versus Off-line Identification 21 1.5 Adaptive and Evolving Systems 22 1.6 Evolving or Evolutionary Systems 23 1.7 Supervised versus Un-supervised Learning 25 1.8 Structure of the Book 26 PART I: Fundamentals 29 2. Fundamentals of Probability Theory 29 2.1 Randomness and Determinism 30 2.2 Frequentistic versus Belief-based Approach 33 2.3 Probability Densities and Moments 33 2.4 Density Estimation
• Kernel-based Approach 37 2.5 Recursive Density Estimation (RDE) 40 2.6 Detecting Novelties/Anomalies/Outliers using RDE 45 2.7 Conclusion 49 3. Fundamentals of Machine Learning and Pattern Recognition 51 3.1 Pre-processing 51 3.1.1 Normalisation and standardisation 52 3.1.2 Orthogonalization of inputs/features
• rPCA method 53 3.2 Clustering 56 3.2.1 Proximity measures and clusters shape 59 3.2.2 Off-line methods 60 3.2.3 Evolving clustering methods 65 3.3 Classification 73 3.3.1 Recursive LDA, rLDA 74 3.4 Conclusion 74 4. Fundamentals of Fuzzy Systems Theory 77 4.1 Fuzzy Sets 77 4.2 Fuzzy Systems, Fuzzy Rules 80 4.2.1 Fuzzy Systems of Zadeh-Mamdani Type 81 4.2.2 Takagi-Sugeno Fuzzy Systems 83 4.3 Fuzzy Systems with Non-parametric Antecedents (AnYa) 87 4.3.1 Architecture 87 4.3.2 Analysis of AnYa 90 4.4 FRB (Off-line) classifiers 91 4.5 Neuro-Fuzzy Systems 94 4.5.1 Neuro-fuzzy system architecture 94 4.5.2 Evolving NFS as a framework for autonomous learning and knowledge extraction from data streams 97 4.5.3 Linguistic interpretation of the NFS 98 4.6 State Space Perspective 99 4.7 Conclusions 100 Part II Methodology of Autonomous Learning Systems 101 5 Evolving System Structure from Streaming Data 101 5.1 Defining system structure based on prior knowledge 101 5.2 Data Space Partitioning 102 5.2.1 Regular partitioning of the data space 103 5.2.2 Data space partitioning through clustering 104 5.2.3. Data space partitioning based on data clouds 105 5.2.4. Importance of partitioning the joint input-output data space 105 5.2.5 Principles of data space partitioning for autonomous machine learning 107 5.2.6 Dynamic data space partitioning
• evolving system structure autonomously 108 5.3 Normalisation and Standardisation of Streaming Data in Evolving Environments 114 5.3.1 Standardization in an Evolving Environment 115 5.3.2 Normalisation in an Evolving Environment 116 5.4 Autonomous Monitoring of the Structure Quality 117 5.4.1 Autonomous Input Variables Selection 117 5.4.2 Autonomous Monitoring of the Age of the Local Sub-model 120 5.4.3 Autonomous Monitoring of the Utility of the Local Sub-model 122 5.4.4 Update of the Cluster Radii 123 5.5 Short- and Long-term Focal Points and Sub-models 124 5.6 Simplification and Interpretability Issues 125 5.7 Conclusion 127 6 Autonomous Learning Parameters of the Local Sub-models 129 6.1 Learning Parameters of Local Sub-models 130 6.2 Global versus Local Learning 131 6.3 Evolving Systems Structure Recursively 133 6.4 Learning Modes 137 6.5 Robustness to Outliers in Autonomous Learning 140 6.6 Conclusions 140 7 Autonomous Predictors, Estimators, Filters, Inferential Sensors 142 7.1 Predictors, Estimators, Filters
• Problem Formulation 142 7.2 Non-linear Regression 144 7.3 Time series 145 7.4 Autonomous Learning Sensors 146 7.4.1 Autonomous Sensors
• Problem Definition 146 7.4.2 A brief Overview of Soft/Intelligent/Inferential Sensors 147 7.4.3 Autonomous Intelligent Sensors (AutoSense) 149 7.4.4 AutoSense Architecture 151 7.4.5 Modes of Operation of AutoSense 152 7.4.6 Autonomous Input Variable Selection 152 7.5 Conclusions 153 8. Autonomous Learning Classifiers 155 8.1 Classifying data streams 155 8.2 Why adapt the classifier structure? 155 8.3 Architecture of Autonomous Classifiers of the Family AutoClassify 157 8.3.1 AutoClassify0 159 8.3.2 AutoClassify1 159 8.4 Learning AutoClassify from Streaming Data 162 8.4.1 Learning AutoClassify0 162 8.4.2 Learning AutoClassify1 163 8.5 Analysis of AutoClassify methods 163 8.6 Conclusions 164 9. Autonomous Learning Controllers 166 9.1 Indirect Adaptive Control Scheme 167 9.2 Evolving Inverse Plant Model from On-line Streaming Data 169 9.3 Evolving Fuzzy Controller Structure from On-line Streaming Data 170 9.4 Examples of using AutoControl 172 9.5 Conclusion 177 10. Collaborative Autonomous Learning Systems 179 10.1 Distributed Intelligence Scenarios 179 10.2 Autonomous Collaborative Learning 181 10.3 Collaborative Autonomous Clustering, AutoCluster by a team of ALSs 183 10.4 Collaborative Autonomous Predictors, Estimators, Filters and AutoSense by a team of ALSs 184 10.5 Collaborative Autonomous Classifiers AutoClassify by a team of ALSs 184 10.6 Superposition of Local Sub-models 185 10.7 Conclusion 186 PART III: Applications of ALS 187 11. Autonomous Learning Sensors for Chemical and Petro-chemical Industries 187 11.1 Case Study 1: Quality of the Products in an Oil Refinery 187 11.1.1 Introduction 187 11.1.2 The current state of the art 188 11.1.3 Problem description 189 11.1.4 The data set 189 11.1.5 AutoSesnse for kerosene quality prediction 191 11.1.6 AutoSense for Abel inflammability test 193 11.2 Case Study 2: Polypropylene Manufacturing 194 11.2.1 Problem description 194 11.2.2 Drift and shift detection by cluster age derivatives 198 11.2.3 Input variables selection 200 11.3 Conclusion 201 12. Autonomous Learning Systems in Mobile Robotics 203 12.1 The mobile robot Pioneer 3DX 203 12.2 Autonomous Classifier for Landmark Recognition 205 12.2.1 Corner detection and simple mapping of an indoor environment through wall following 207 12.2.2 Outdoor landmark detection based on visual input information 210 12.2.3 VideoDiaries 214 12.2.4 Collaborative scenario 217 12.3 Autonomous Leader Follower 220 12.4 Results Analysis 223 13. Autonomous Novelty Detection and Object Tracking in Video Streams 224 13.1 Problem Definition 224 13.2 Background subtraction and KDE for detecting visual novelties 225 13.2.1 Background subtraction method 225 13.2.2 Challenges 226 13.2.3 Parametric versus non-parametric approaches 229 13.2.4 Kernel Density Estimation method 230 13.3 Detecting Visual novelties with RDE Method 231 13.4 Object Identification in Image Frames using RDE 232 13.5 Real-time Tracking in Video Streams using ALS 234 13.6 Conclusion 237 14. Modelling Evolving User Behaviour with ALS 239 14.1 User Behaviour as an evolving phenomenon 239 14.2 Designing the User Behaviour Profile 241 14.3 Applying AutoClassify0 for modelling evolving user behaviour 244 14.4 Case studies 245 14.4.1 Users of UNIX commands 245 14.4.2 Modelling activity of people in a smart home environment 247 14.4.3 Automatic scene recognition 249 14.5 Conclusions 252 15. Epilogue 254 15.1 Conclusions 254 15.2 Open Problems 258 15.3 Future Directions 259 Bibliography 261 Index 274 Glossary 291 Appendices 295 A. Mathematical Foundations 296 A1 Probability distributions 297 A2 Basic matrix properties 300 B. Pseudo-code of the basic algorithms 302 B1 Mean shift with Epanechnikov kernel 302 B2 AutoCluster 304 B3 ELM 305 B4 AutoPredict 307 B5 AutoSense 308 B6 AutoClassify0 309 B7 AutoClassify1 311 B8 AutoControl 313.
• (source: Nielsen Book Data)

• Forewords xi Preface xix About the Author xxiii 1 Introduction 1 1.1 Autonomous Systems 3 1.2 The Role of Machine Learning in Autonomous Systems 4 1.3 System Identification an Abstract Model of the Real World 6 1.4 Online versus Offline Identification 9 1.5 Adaptive and Evolving Systems 10 1.6 Evolving or Evolutionary Systems 11 1.7 Supervised versus Unsupervised Learning 13 1.8 Structure of the Book 14 PART I FUNDAMENTALS 2 Fundamentals of Probability Theory 19 2.1 Randomness and Determinism 20 2.2 Frequentistic versus Belief-Based Approach 22 2.3 Probability Densities and Moments 23 2.4 Density Estimation Kernel-Based Approach 26 2.5 Recursive Density Estimation (RDE) 28 2.6 Detecting Novelties/Anomalies/Outliers using RDE 32 2.7 Conclusions 36 3 Fundamentals of Machine Learning and Pattern Recognition 37 3.1 Preprocessing 37 3.2 Clustering 42 3.3 Classification 56 3.4 Conclusions 58 4 Fundamentals of Fuzzy Systems Theory 61 4.1 Fuzzy Sets 61 4.2 Fuzzy Systems, Fuzzy Rules 64 4.3 Fuzzy Systems with Nonparametric Antecedents (AnYa) 69 4.4 FRB (Offline) Classifiers 73 4.5 Neurofuzzy Systems 75 4.6 State Space Perspective 79 4.7 Conclusions 81 PART II METHODOLOGY OF AUTONOMOUS LEARNING SYSTEMS 5 Evolving System Structure from Streaming Data 85 5.1 Defining System Structure Based on Prior Knowledge 85 5.2 Data Space Partitioning 86 5.3 Normalisation and Standardisation of Streaming Data in an Evolving Environment 96 5.4 Autonomous Monitoring of the Structure Quality 98 5.5 Short- and Long-Term Focal Points and Submodels 104 5.6 Simplification and Interpretability Issues 105 5.7 Conclusions 107 6 Autonomous Learning Parameters of the Local Submodels 109 6.1 Learning Parameters of Local Submodels 110 6.2 Global versus Local Learning 111 6.3 Evolving Systems Structure Recursively 113 6.4 Learning Modes 116 6.5 Robustness to Outliers in Autonomous Learning 118 6.6 Conclusions 118 7 Autonomous Predictors, Estimators, Filters, Inferential Sensors 121 7.1 Predictors, Estimators, Filters Problem Formulation 121 7.2 Nonlinear Regression 123 7.3 Time Series 124 7.4 Autonomous Learning Sensors 125 7.5 Conclusions 131 8 Autonomous Learning Classifiers 133 8.1 Classifying Data Streams 133 8.2 Why Adapt the Classifier Structure? 134 8.3 Architecture of Autonomous Classifiers of the Family AutoClassify 135 8.4 Learning AutoClassify from Streaming Data 139 8.5 Analysis of AutoClassify 140 8.6 Conclusions 140 9 Autonomous Learning Controllers 143 9.1 Indirect Adaptive Control Scheme 144 9.2 Evolving Inverse Plant Model from Online Streaming Data 145 9.3 Evolving Fuzzy Controller Structure from Online Streaming Data 147 9.4 Examples of Using AutoControl 148 9.5 Conclusions 153 10 Collaborative Autonomous Learning Systems 155 10.1 Distributed Intelligence Scenarios 155 10.2 Autonomous Collaborative Learning 157 10.3 Collaborative Autonomous Clustering, AutoCluster by a Team of ALSs 158 10.4 Collaborative Autonomous Predictors, Estimators, Filters and AutoSense by a Team of ALSs 159 10.5 Collaborative Autonomous Classifiers AutoClassify by a Team of ALSs 160 10.6 Superposition of Local Submodels 161 10.7 Conclusions 161 PART III APPLICATIONS OF ALS 11 Autonomous Learning Sensors for Chemical and Petrochemical Industries 165 11.1 Case Study
• 1: Quality of the Products in an Oil Refinery 165 11.2 Case Study
• 2: Polypropylene Manufacturing 172 11.3 Conclusions 178 12 Autonomous Learning Systems in Mobile Robotics 179 12.1 The Mobile Robot Pioneer 3DX 179 12.2 Autonomous Classifier for Landmark Recognition 180 12.3 Autonomous Leader Follower 193 12.4 Results Analysis 196 13 Autonomous Novelty Detection and Object Tracking in Video Streams 197 13.1 Problem Definition 197 13.2 Background Subtraction and KDE for Detecting Visual Novelties 198 13.3 Detecting Visual Novelties with the RDE Method 203 13.4 Object Identification in Image Frames Using RDE 204 13.5 Real-time Tracking in Video Streams Using ALS 206 13.6 Conclusions 209 14 Modelling Evolving User Behaviour with ALS 211 14.1 User Behaviour as an Evolving Phenomenon 211 14.2 Designing the User Behaviour Profile 212 14.3 Applying AutoClassify0 for Modelling Evolving User Behaviour 215 14.4 Case Studies 216 14.5 Conclusions 221 15 Epilogue 223 15.1 Conclusions 223 15.2 Open Problems 227 15.3 Future Directions 227 APPENDICES Appendix A Mathematical Foundations 231 Appendix B Pseudocode of the Basic Algorithms 235 References 245 Glossary 259 Index 263.
• (source: Nielsen Book Data)

Addresses the techniques and challenges of autonomous learning systems and illustrates the practical relevance of the approach with a wide range of applications Autonomous Learning Systems is the result of over a decade of focused research and studies in this emerging area which spans a number of well-known and well-established disciplines that include machine learning, system identification, data mining, fuzzy logic and fuzzy systems, neural networks, neuro-fuzzy systems, control theory and pattern recognition. The evolution of these systems has been both industry-driven with an increasing demand from sectors such as defence and security, aerospace and advanced process industries, bio-medicine and intelligent transportation, and research-driven - there is a strong trend of innovation of all of the above well-established research disciplines that is linked to their on-line and real-time application; their adaptability and flexibility. Providing an introduction to the key technologies, detailed technical explanations of the methodology, and an illustration of the practical relevance of the approach with a wide range of applications, this book addresses the challenges of autonomous learning systems with a systematic approach that lays the foundations for a fast growing area of research that will underpin a range of technological applications vital to both industry and society. Including a wide range of applications, illustrations and software code (MatLAB(r)) that can be used for further research it constitutes a valuable 'one-stop-shop' for academics, researchers, practicing engineers, computer specialists and defence and industry experts.
(source: Nielsen Book Data)
Autonomous Learning Systems is the result of over a decade of focused research and studies in this emerging area which spans a number of well-known and well-established disciplines that include machine learning, system identification, data mining, fuzzy logic, neural networks, neuro-fuzzy systems, control theory and pattern recognition. The evolution of these systems has been both industry-driven with an increasing demand from sectors such as defence and security, aerospace and advanced process industries, bio-medicine and intelligent transportation, as well as research-driven there is a strong trend of innovation of all of the above well-established research disciplines that is linked to their on-line and real-time application; their adaptability and flexibility. Providing an introduction to the key technologies, detailed technical explanations of the methodology, and an illustration of the practical relevance of the approach with a wide range of applications, this book addresses the challenges of autonomous learning systems with a systematic approach that lays the foundations for a fast growing area of research that will underpin a range of technological applications vital to both industry and society. Key features: * Presents the subject systematically from explaining the fundamentals to illustrating the proposed approach with numerous applications. * Covers a wide range of applications in fields including unmanned vehicles/robotics, oil refineries, chemical industry, evolving user behaviour and activity recognition. * Reviews traditional fields including clustering, classification, control, fault detection and anomaly detection, filtering and estimation through the prism of evolving and autonomously learning mechanisms. * Accompanied by a website hosting additional material, including the software toolbox and lecture notes. Autonomous Learning Systems provides a one-stop shop on the subject for academics, students, researchers and practicing engineers. It is also a valuable reference for Government agencies and software developers.
(source: Nielsen Book Data)

• Table of Contents Introduction to Markov Process Hidden Markov Models State Inference: Predicting the states Parameter Inference using Maximum Likelihood Parameter Inference using Bayesian Approach Time Series: Predicting Stock Prices Natural Language Processing: Teaching machines to talk 2D-HMM for Image Processing Reinforcement Learning: Teaching a robot to cross a maze.
• (source: Nielsen Book Data)

Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearn Key Features Build a variety of Hidden Markov Models (HMM) Create and apply models to any sequence of data to analyze, predict, and extract valuable insights Use natural language processing (NLP) techniques and 2D-HMM model for image segmentation Book DescriptionHidden Markov Model (HMM) is a statistical model based on the Markov chain concept. Hands-On Markov Models with Python helps you get to grips with HMMs and different inference algorithms by working on real-world problems. The hands-on examples explored in the book help you simplify the process flow in machine learning by using Markov model concepts, thereby making it accessible to everyone. Once you've covered the basic concepts of Markov chains, you'll get insights into Markov processes, models, and types with the help of practical examples. After grasping these fundamentals, you'll move on to learning about the different algorithms used in inferences and applying them in state and parameter inference. In addition to this, you'll explore the Bayesian approach of inference and learn how to apply it in HMMs. In further chapters, you'll discover how to use HMMs in time series analysis and natural language processing (NLP) using Python. You'll also learn to apply HMM to image processing using 2D-HMM to segment images. Finally, you'll understand how to apply HMM for reinforcement learning (RL) with the help of Q-Learning, and use this technique for single-stock and multi-stock algorithmic trading. By the end of this book, you will have grasped how to build your own Markov and hidden Markov models on complex datasets in order to apply them to projects. What you will learn Explore a balance of both theoretical and practical aspects of HMM Implement HMMs using different datasets in Python using different packages Understand multiple inference algorithms and how to select the right algorithm to resolve your problems Develop a Bayesian approach to inference in HMMs Implement HMMs in finance, natural language processing (NLP), and image processing Determine the most likely sequence of hidden states in an HMM using the Viterbi algorithm Who this book is forHands-On Markov Models with Python is for you if you are a data analyst, data scientist, or machine learning developer and want to enhance your machine learning knowledge and skills. This book will also help you build your own hidden Markov models by applying them to any sequence of data. Basic knowledge of machine learning and the Python programming language is expected to get the most out of the book.
(source: Nielsen Book Data)

• Learning sequential data with the help of linear systems
• A spiking neural network for personalised modelling of Electrogastogrophy (EGG)
• Improving generalization abilities of maximal average margin classifiers
• Finding small sets of random Fourier features for shift-invariant kernel approximation
• Incremental construction of low-dimensional data representations
• Soft-constrained nonparametric density estimation with artificial neural networks
• Density based clustering via dominant sets
• Co-training with credal models
• Interpretable classifiers in precision medicine: feature selection and multi-class categorization
• On the evaluation of tensor-based representations for optimum-pathforest classification
• On the harmony search using quaternions
• Learning parameters in deep belief networks through firefly algorithm
• Towards effective classification of imbalanced data with convolutional neural networks
• On CPU performance optimization of restricted Boltzmann machine and convolutional RBM
• Comparing incremental learning strategies for convolutional neural networks
• Approximation of graph edit distance by means of a utility matrix
• Time series classification in reservoir- and model-space: a comparison
• Objectness scoring and detection proposals in forward-Looking sonar images with convolutional neural networks
• Background categorization for automatic animal detection in aerial videos using neural networks
• Predictive segmentation using multichannel neural networks in Arabic OCR system
• Quad-tree based image segmentation and feature extraction to recognize online handwritten Bangla characters
• A hybrid recurrent neural network/dynamic probabilistic graphical model predictor of the disulfide bonding state of cysteines from the primary structure of proteins
• Using radial basis function neural networks for continuous anddiscrete pain estimation from bio-physiological signals
• Active learning for speech event detection in HCI
• Emotion recognition in speech with deep learning architectures
• On gestures and postural behavior as a modality in ensemble methods
• Machine learning driven heart rate detection with camera photoplethysmography in time domain.

This book constitutes the refereed proceedings of the 7th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2016, held in Ulm, Germany, in September 2016. The 25 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 32 submissions for inclusion in this volume. The workshop will act as a major forum for international researchers and practitioners working in all areas of neural network- and machine learning-based pattern recognition to present and discuss the latest research, results, and ideas in these areas.

• Intro
• Preface
• Organization
• Contents
• Invited Papers
• What's Wrong with Computer Vision?
• 1 Introduction
• 2 Top Ten Questions a Theory on Vision Should Address
• 3 Hierarchical Description of Visual Tasks
• 3.1 Pixel-Wise and Abstract Visual Interpretations
• 3.2 The Interwound Story of Vision and Language
• 3.3 When Vision Collapses to Classification
• 4 Conclusions
• References
• Deep Learning in the Wild
• 1 Introduction
• 2 Face Matching
• 3 Print Media Monitoring
• 4 Visual Quality Control
• 5 Music Scanning
• 6 Game Playing
• 7 Automated Machine Learning

• 8 Conclusions
• References
• Learning Algorithms and Architectures
• Effect of Equality Constraints to Unconstrained Large Margin Distribution Machines
• 1 Introduction
• 2 Least Squares Support Vector Machines
• 3 Large Margin Distribution Machines and Their Variants
• 3.1 Large Margin Distribution Machines
• 3.2 Least Squares Large Margin Distribution Machines
• 3.3 Unconstrained Large Margin Distribution Machines
• 4 Performance Evaluation
• 4.1 Conditions for Experiment
• 4.2 Results for Two-Class Problems
• 5 Conclusions
• References
• DLL: A Fast Deep Neural Network Library

• 1 Introduction
• 2 DLL: Deep Learning Library
• 2.1 Performance
• 2.2 Example
• 3 Experimental Evaluation
• 4 MNIST
• 4.1 Fully-Connected Neural Network
• 4.2 Convolutional Neural Network
• 5 CIFAR-10
• 6 ImageNet
• 7 Conclusion and Future Work
• References
• Selecting Features from Foreign Classes
• 1 Introduction
• 2 Methods
• 2.1 Learning from Context Classes
• 2.2 Foreign Class Combinations
• 3 Experiments
• 3.1 Datasets
• 4 Results
• 5 Discussion and Conclusion
• References
• A Refinement Algorithm for Deep Learning via Error-Driven Propagation of Target Outputs

• 1 Introduction
• 2 Error-Driven Target Propagation: Formalization of the Algorithms
• 2.1 The Inversion Net
• 2.2 Refinement of Deep Learning via Target Propagation
• 3 Experiments
• 4 Conclusions
• References
• Combining Deep Learning and Symbolic Processing for Extracting Knowledge from Raw Text
• 1 Introduction
• 2 Model
• 2.1 Semantic Features
• 2.2 Logic Constraints
• 2.3 Segmentation
• 3 Experiments
• 4 Conclusions
• References
• SeNA-CNN: Overcoming Catastrophic Forgetting in Convolutional Neural Networks by Selective Network Augmentation
• 1 Introduction
• 2 Related Work

• 3 Proposed Method
• 4 Experiments
• 4.1 Network Architecture
• 4.2 Training Methodology
• 4.3 Isolated Learning
• 4.4 Adding New Tasks to the Models
• 4.5 Three Tasks Scenario
• 5 Conclusion
• References
• Classification Uncertainty of Deep Neural Networks Based on Gradient Information
• 1 Introduction
• 2 Entropy, Softmax Baseline and Gradient Metrics
• 3 Meta Classification
• A Benchmark Between Maximum Softmax Probability and Gradient Metrics
• 4 Recognition of Unlearned Concepts
• 5 Meta Classification with Known Unknowns
• 6 Conclusion and Outlook
• References

This book constitutes the refereed proceedings of the 8th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2018, held in Siena, Italy, in September 2018. The 29 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 35 submissions. The papers present and discuss the latest research in all areas of neural network- and machine learning-based pattern recognition. They are organized in two sections: learning algorithms and architectures, and applications. Chapter "Bounded Rational Decision-Making with Adaptive Neural Network Priors" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
(source: Nielsen Book Data)

• Deep Learning Methods for Image Guidance in Radiation Therapy Intentional Image Similarity Search.- Sttructured (De)composable Representations Trained with Neural Networks.- Long Distance Relationships without Time Travel: Boosting the Performance of a Sparse Predictive Autoencoder in Sequence Modeling.- Improving Accuracy and Efficiency of Object Detection Algorithms using Multiscale Feature Aggregation Plugins.- Abstract Echo State Networks.- Minimal Complexity Support Vector Machines.- Named Entity Disambiguation at Scale.- Geometric Attention for Prediction of Differential Properties in 3D Point Clouds.- How (Not) to Measure Bias in Face Recognition Networks.-Feature Extraction: A Time Window Analysis based on the X-ITE Pain Database.- Pain Intensity Recognition - An Analysis of Short-Time Sequences in a Real-World Scenario.- A deep learning approach for efficient registration of dual view mammography.- Deep Transfer Learning for Texture Classification in Colorectal Cancer Histology.- Applications of Generative Adversarial Networks to Dermatologic Imaging.- Typing Plasmids with Distributed Sequence Representation.- KP-YOLO: a modification of YOLO algorithm for the keypoint-based detection of QR Codes.- Using Mask R-CNN for Image-Based Wear Classification of Solid Carbide Milling and Drilling Tools.- A Hybrid Deep Learning Approach For Forecasting Air Temperature.- Using CNNs to optimize numerical simulations in geotechnical engineering.- Going for 2D or 3D? Investigating various Machine Learning Approaches for Peach Variety Identification.- A Transfer Learning End-to-End Arabic Text-To-Speech (TTS) Deep Architecture.- ML-Based Trading Models: An investigation during COVID-19 pandemic crisis.- iNNvestigate-GUI - Explaining Neural Networks Through an Interactive Visualization Tool.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 9th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2020, held in Winterthur, Switzerland, in September 2020. The conference was held virtually due to the COVID-19 pandemic. The 22 revised full papers presented were carefully reviewed and selected from 34 submissions. The papers present and discuss the latest research in all areas of neural network-and machine learning-based pattern recognition. They are organized in two sections: learning algorithms and architectures, and applications.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 32nd Annual IFIP WG 11.3 International Working Conference on Data and Applications Security and Privacy, DBSec 2018, held in Bergamo, Italy, in July 2018. The 16 full papers and 5 short papers presented were carefully reviewed and selected from 50 submissions. The papers present high-quality original research from academia, industry, and government on theoretical and practical aspects of information security. They are organized in topical sections on administration, access control policies, privacy-preserving access and computation, integrity and user interaction, security analysis and private evaluation, fixing vulnerabilities, and networked systems.
(source: Nielsen Book Data)

• Network and Cyber-physical Systems Security.- Modeling and Mitigating Security Threats in Network Functions Virtualization (NFV).- Managing Secure Inter-slice Communication in 5G Network Slice Chains.- Proactively Extracting IoT Device Capabilities: An Application to Smart Homes.- Security Enumerations for Cyber-Physical Systems.- Information Flow and Access Control.- Inference-Proof Monotonic Query Evaluation and View Generation Reconsidered.- Network Functions Virtualisation Access Control as a Service.- Effective Access Control in Shared-Operator Multi-tenant Data Stream Management Systems.- Information Flow Security Certification for SPARK Programs.- Privacy-preserving Computation.- Provably Privacy-Preserving Distributed Data Aggregation in Smart Grids.- Non-Interactive Private Decision Tree Evaluation.- Privacy-preserving Anomaly Detection using Synthetic Data.- Local Differentially Private Matrix Factorization with MoG for Recommendations.- Visualization and Analytics for Security.- Designing a Decision-Support Visualization for Live Digital Forensic Investigations.- Predictive Analytics to Prevent Voice Over IP International Revenue Sharing Fraud.- PUA Detection Based on Bundle Installer Characteristics.- ML-supported Identification and Prioritization of Threats in the OVVL Threat Modelling Tool.- Spatial Systems and Crowdsourcing Security.- Enhancing the Performance of Spatial Queries on Encrypted Data through Graph Embedding.- Crowdsourcing under Data Poisoning Attacks: A Comparative Study.- Self-Enhancing GPS-based Authentication Using Corresponding Address.- Secure Outsourcing and Privacy.- GOOSE: A Secure Framework for Graph Outsourcing and SPARQL Evaluation.- SGX-IR: Secure Information Retrieval with Trusted Processors.- Measuring Readability of Privacy Policies.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 34th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy, DBSec 2020, held in Regensburg, Germany, in June 2020.* The 14 full papers and 8 short papers presented were carefully reviewed and selected from 39 submissions. The papers present high-quality original research from academia, industry, and government on theoretical and practical aspects of information security. They are organized in topical sections named network and cyber-physical systems security; information flow and access control; privacy-preserving computation; visualization and analytics for security; spatial systems and crowdsourcing security; and secure outsourcing and privacy. *The conference was held virtually due to the COVID-19 pandemic.
(source: Nielsen Book Data)

• A New Method of Breakpoint Connection for Human Skeleton Image
• Insult Detection in Social Network Comments using Possibilistic based Fusion Approach
• What Information in Software Historical Repositories Do We Need to Support Software Maintenance Tasks? An Approach Based on Topic Model
• Evaluation Framework for the Dependability of Ubiquitous Learning Environment
• Improving Content Recommendation in Social Streams via Interest Model
• Performance Evaluation of Unsupervised Learning Techniques for Intrusion Detection in Mobile Ad Hoc Networks
• Live Migration Performance Modelling for Virtual Machines with Resizable Memory
• A Heuristic Algorithm forWorkflow-Based Job Scheduling in Decentralized Distributed Systems with Heterogeneous Resources
• Novel Data Integrity Verification Schemes in Cloud Storage
• Generation of Assurance Cases For Medical Devices
• A Survey on the Categories of Service Value/Quality/Satisfactory Factors
• Effective Domain Modeling for Mobile Business AHMS (Adaptive Human Management Systems) Requirements.

• Intro; Foreword; Contents; Contributors; 1 A New Method of Breakpoint Connection for Human Skeleton Image; 1 Introduction; 2 Image Preprocessing; 3 Basic Elements; 3.1 Neighborhood Layer; 3.2 Upper Left Neighborhood and Lower Right Neighborhood; 3.3 Breakpoint Type; 3.4 Available Connection Point; 4 Breakpoint Connection Function; 4.1 ΔW = 0; 4.2 ΔW neq 0; 5 Procedure of Breakpoint Connection; 5.1 Search Skeleton Breakpoint; 5.2 Search Available Connection Point; 5.3 Connect Breakpoint; 6 Experimental Results; 7 Conclusion; References

This edited book presents scientific results of the 13th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2014) which was held on June 4-6, 2014 in Taiyuan, China. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. Research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them. The conference organizers selected the best papers from those papers accepted for presentation at the conference. The papers were chosen based on review scores submitted by members of the program committee, and underwent further rigorous rounds of review. This publication captures 14 of the conference's most promising papers, and we impatiently await the important contributions that we know these authors will bring to the field of computer and information science.

• Modelling of Underwater Robots.- Dynamic Control of 6-DOF AUVs.- Fault Detection/Tolerance Strategies for AUVs and ROVs.- Experiments of Dynamic Control of a 6-DOF AUV.- Kinematic Control of UVMSs.- Dynamic Control of UVMSs.- Interaction Control of UVMSs.- Dynamic Control of 6-DOF AUVs.- Fault Detection/Tolerance Strategies for AUVs and ROVs.- Experiments of Dynamic Control of a 6-DOF AUV.- Kinematic Control of UVMSs.- Dynamic Control of UVMSs.- Interaction Control of UVMSs.
• (source: Nielsen Book Data)

A classic in underwater robotics. One of the first volumes in the "Springer Tracts in Advanced Robotics" series, it has been a bestseller through the previous three editions. Fifteen years after the publication of the first edition, the fourth edition comes to print. The book addresses the main control aspects in underwater manipulation tasks. With respect to the third edition, it has been revised, extended and some concepts better clustered. The mathematical model with significant impact on the control strategy is discussed. The problem of controlling a 6-degrees-of-freedoms autonomous underwater vehicle is investigated and a survey of fault detection/tolerant strategies for unmanned underwater vehicles is provided. Inverse kinematics, dynamic and interaction control for underwater vehicle-manipulator systems are then discussed. The code used to generate most of the numerical simulations is made available and briefly discussed.
(source: Nielsen Book Data)

• A Bearing-only Pattern Formation Algorithm for Swarm Robotics
• A Macroscopic Privacy Model for Heterogeneous Robot Swarms
• A New Continuous Model for Segregation Implemented and Analyzed on Swarm Robots
• A Study of Archiving Strategies in Multi-objective PSO for Molecular Docking
• Ant Colony Optimisation-based Classification using Two-dimensional Polygons
• Collective Perception of Environmental Features in a Robot Swarm
• Communication Diversity in Particle Swarm Optimizers
• Continuous Time Gathering of Agents with Limited Visibility and Bearing-only Sensing
• Design and Analysis of Proximate Mechanisms for Cooperative Transport in Real Robots
• Dynamic Task Partitioning for Foraging Robot Swarms
• Human-robot Swarm Interaction with Limited Situational Awareness
• Monotonicity in Ant Colony Classification Algorithms
• Observing the Effects of Overdesign in the Automatic Design of Control Software for Robot Swarms
• Parameter Selection in Particle Swarm Optimisation from Stochastic Stability Analysis
• Population Coding: A New Design Paradigm for Embodied Distributed Systems
• Random Walks in Swarm Robotics: An Experiment with Kilobots
• Synthesizing Rulesets for Programmable Robotic Self-assembly: A Case Study using Floating Miniaturized Robots
• Using Ant Colony Optimization to Build Cluster-based Classification Systems
• A Swarm Intelligence Approach in Undersampling Majority Class
• Optimizing PolyACO Training with GPU-based Parallelization
• Motion Reconstruction of Swarm-like Self-organized Motor Bike Traffic from Public Videos
• On Heterogeneity in Foraging by Ant-like Colony: How Local Affects Global and Vice Versa
• On Stochastic Broadcast Control of Swarms
• Route Assignment for Autonomous Vehicles
• Stealing Items More Efficiently with Ants: A Swarm Intelligence Approach to the Travelling Thief Problem
• Achieving Synchronisation in Swarm Robotics: Applying Networked Q-Learning to Production Line Automata
• Autonomous Task Allocation for Swarm Robotic Systems Using Hierarchical Strategy
• Avoidance Strategies for Particle Swarm Optimisation in Power Generation Scheduling
• Clustering with the ACOR Algorithm
• Consideration Regarding the Reduction of Reality Gap in Evolutionary Swarm Robotics
• Hybrid Deployment Algorithm of Swarm Robots for Wireless Mesh Network
• On the Definition of Self-organizing Systems: Relevance of Positive/Negative Feedback and Fluctuations
• Particle Swarm Optimisation with Diversity Influenced Gradually Increasing Neighbourhoods.

This book constitutes the proceedings of the 10th International Conference on Swarm Intelligence, ANTS 2016, held in Brussels, Belgium, in September 2016. The 18 full papers and 7 short papers presented in this volume were carefully reviewed and selected from 47 submissions. They are devoted to the field of swarm intelligence as a whole, without any bias towards specific research directions.

• Full Papers.- Short Papers.- Extended Abstracts.
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This book constitutes the proceedings of the 11th International Conference on Swarm Intelligence, ANTS 2018, held in Rome, Italy, in October 2018. The 24 full papers and 12 short papers presented in this volume were carefully reviewed and selected from 69 submissions. They are devoted to the field of swarm intelligence as a whole, without any bias towards specific research directions.
(source: Nielsen Book Data)

• Types.- Program Analysis.- Tools.- Functional Programs and Probabilistic Programs.- Verification.- Logic.- Continuation and Model Checking.
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This book constitutes the proceedings of the 16th Asian Symposium on Programming Languages and Systems, APLAS 2018, held in Wellington, New Zealand, in December 2018.The 22 papers presented in this volume were carefully reviewed and selected from 51 submissions. They are organized in topical sections named: types; program analysis; tools; functional programs and probabilistic programs; verification; logic; and continuation and model checking.
(source: Nielsen Book Data)

• Mobile web data analytics.- Big spatial data and urban computing.- Graph data management and analytics.- Mobility analytics from spatial and social data.
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed post-conference proceedings of the First APWeb-WAIM 2017 Workshops, held jointly with the First International Joint Conference APWeb-WAIM 2017, held in Beijing, China, in July 2017. The 25 full papers presented were carefully reviewed and selected from 40 submissions. The papers originating from six workshops present cutting-edge ideas, results, experiences, techniques, and tools from all aspects of web data management with the focus on mobile web data analytics; big spatial data and urban computing; graph data management and analytics; mobility analytics from spatial and social data.
(source: Nielsen Book Data)

• Spatial data processing and data quality.- Graph data processing.- Data mining, privacy and semantic analysis.- Text and log data management.-Social networks.- Data mining and data streams.- Query processing.- Topic modeling.- Machine learning.- Recommendation systems.- Distributed data processing and applications.- Machine learning and optimization.
• (source: Nielsen Book Data)

This two -volume set, LNCS 10366 and 10367, constitutes the thoroughly refereed proceedings of the First International Joint Conference, APWeb-WAIM 2017, held in Beijing, China in July 2017. The 44 full papers presented together with 32 short papers and 10 demonstrations papers were carefully reviewed and selected from 240 submissions. The papers are organized around the following topics: spatial data processing and data quality; graph data processing; data mining, privacy and semantic analysis; text and log data management; social networks; data mining and data streams; query processing; topic modeling; machine learning; recommendation systems; distributed data processing and applications; machine learning and optimization.
(source: Nielsen Book Data)

• Spatial data processing and data quality.- Graph data processing.- Data mining, privacy and semantic analysis.- Text and log data management.-Social networks.- Data mining and data streams.- Query processing.- Topic modeling.- Machine learning.- Recommendation systems.- Distributed data processing and applications.- Machine learning and optimization. .
• (source: Nielsen Book Data)

This two -volume set, LNCS 10366 and 10367, constitutes the thoroughly refereed proceedings of the First International Joint Conference, APWeb-WAIM 2017, held in Beijing, China in July 2017. The 44 full papers presented together with 32 short papers and 10 demonstrations papers were carefully reviewed and selected from 240 submissions. The papers are organized around the following topics: spatial data processing and data quality; graph data processing; data mining, privacy and semantic analysis; text and log data management; social networks; data mining and data streams; query processing; topic modeling; machine learning; recommendation systems; distributed data processing and applications; machine learning and optimization.
(source: Nielsen Book Data)