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• Evolutionary Extreme Learning Machine Based Weighted Fuzzy-rough Nearest-neighbour Algorithm.-NNRW-based algorithm selection for software model checking.- An extreme learning machine method for diagnosis of patellofemoral pain syndrome.- Extreme Learning Machines for Signature Verification.-Website Classification from Webpage Renders.- ELM Algorithms Optimized by WOA for Motor Imagery Classification.- The Octonion Extreme Learning Machine.- Scikit-ELM: an Extreme Learning Machine toolbox for dynamic and scalable learning.- High-performance ELM for Memory Constrained Edge Computing Devices with Metal Performance Shaders.- Validating Untrained Human Annotations using Extreme Learning Machines.- ELM Feature Selection and SOM Data Visualization for Nursing Survey Dataset.
• (source: Nielsen Book Data)

This book contains some selected papers from the International Conference on Extreme Learning Machine 2019, which was held in Yangzhou, China, December 14-16, 2019. Extreme Learning Machines (ELMs) aim to enable pervasive learning and pervasive intelligence. As advocated by ELM theories, it is exciting to see the convergence of machine learning and biological learning from the long-term point of view. ELM may be one of the fundamental 'learning particles' filling the gaps between machine learning and biological learning (of which activation functions are even unknown). ELM represents a suite of (machine and biological) learning techniques in which hidden neurons need not be tuned: inherited from their ancestors or randomly generated. ELM learning theories show that effective learning algorithms can be derived based on randomly generated hidden neurons (biological neurons, artificial neurons, wavelets, Fourier series, etc) as long as they are nonlinear piecewise continuous, independent of training data and application environments. Increasingly, evidence from neuroscience suggests that similar principles apply in biological learning systems. ELM theories and algorithms argue that "random hidden neurons" capture an essential aspect of biological learning mechanisms as well as the intuitive sense that the efficiency of biological learning need not rely on computing power of neurons. ELM theories thus hint at possible reasons why the brain is more intelligent and effective than current computers. The main theme of ELM2019 is Hierarchical ELM, AI for IoT, Synergy of Machine Learning and Biological Learning. This conference provides a forum for academics, researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique and brain learning. This book covers theories, algorithms and applications of ELM. It gives readers a glance of the most recent advances of ELM.
(source: Nielsen Book Data)

• Improvement in Side Lobe Reduction in FIR Filter Design Using Proposed Hybrid Blackman Window
• Low Frequency Stochastic Electromagnetic Field Observed in the Ionosphere Possibly Associated with an Earthquake Activity
• A Review on Role of Solar Drying Technology in Sustainable Development
• Role of Advance Solar Desalination Technique for Sustainable Development
• Effect of Reinforced Nano-Composites on AMC Solidification Curve
• An Efficient Performance of Enhanced Bellman-Ford Algorithm in Wireless Sensor Network Using K-Medoid Clustering
• Wavelet Based Compression of Acne Face Images with Automatic Selection and Lossless Compression of Acne Affected Region
• Absolute Ionization Cross Sections of Hydrogen Chloride Gaseous Molecule by Electron Impact. Plus 43 other papers.

This book delves into various solution paradigms such as artificial neural network, support vector machine, wavelet transforms, evolutionary computing, swarm intelligence. During the last decade, novel solution technologies based on human and species intelligence have gained immense popularity due to their flexible and unconventional approach. New analytical tools are also being developed to handle big data processing and smart decision making. The idea behind compiling this work is to familiarize researchers, academicians, industry persons and students with various applications of intelligent techniques for producing sustainable, cost-effective and robust solutions of frequently encountered complex, real-world problems in engineering and science disciplines. The practical problems in smart grids, communication, waste management, elimination of harmful elements from nature, etc., are identified, and smart and optimal solutions are proposed. .
(source: Nielsen Book Data)

This book contains some selected papers from the International Conference on Extreme Learning Machine 2018, which was held in Singapore, November 21-23, 2018. This conference provided a forum for academics, researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique and brain learning. Extreme Learning Machines (ELM) aims to enable pervasive learning and pervasive intelligence. As advocated by ELM theories, it is exciting to see the convergence of machine learning and biological learning from the long-term point of view. ELM may be one of the fundamental "learning particles" filling the gaps between machine learning and biological learning (of which activation functions are even unknown). ELM represents a suite of (machine and biological) learning techniques in which hidden neurons need not be tuned: inherited from their ancestors or randomly generated. ELM learning theories show that effective learning algorithms can be derived based on randomly generated hidden neurons (biological neurons, artificial neurons, wavelets, Fourier series, etc.) as long as they are nonlinear piecewise continuous, independent of training data and application environments. Increasingly, evidence from neuroscience suggests that similar principles apply in biological learning systems. ELM theories and algorithms argue that "random hidden neurons" capture an essential aspect of biological learning mechanisms as well as the intuitive sense that the efficiency of biological learning need not rely on computing power of neurons. ELM theories thus hint at possible reasons why the brain is more intelligent and effective than current computers. The main theme of ELM2018 is Hierarchical ELM, AI for IoT, Synergy of Machine Learning and Biological Learning. This book covers theories, algorithms and applications of ELM. It gives readers a glance at the most recent advances of ELM. .
(source: Nielsen Book Data)

• Ensemble Learning to Generate Virtual Data for Pattern Recognition and Its Possibility of Application to Strategy Inference in Table Tennis.- The rise of smart contract and its complex impacts on the digital ecosystem.- Automatic Collision Avoidance Using Deep Reinforcement Learning with Grid Sensor.- Two way allocation methods in SAVS for large depopulated area.- Proposal of mathematical model of prediction of human movement using position information.- Public transport smart card data analysis using tucker decomposition.- Estimating impressions for clothing, landscape, and indoor images using CNN.- Block-Based Neural Network High Speed Optimization.- An Improved Auto-encoder Based on 2-level Prioritized Experience Replay for High Dimension Skewed Data.- Multi-Task Learning Using Online Fine-Tuning Considering the Importance of Each Filter.- Visualizing Deep Q-Learning to Understanding Behavior of Swarm Robotic System.- A Study of Upper Tropospheric Circulations over the Northern Hemisphere Prediction Using Multivariate Features by ConvLSTM.- Learning localization skills of soccer robot using by simulated omni-vision camera.- Examination of analysis method of opinion distribution in News media transferred on Web.- Opinion distribution analysis of video media related to social life.- Quantitative consideration of spatio-temporal information on education and culture in Japan using telephone directory archive data.
• (source: Nielsen Book Data)

This book presents selected papers from the 23rd Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2019), which was held in Tottori, Japan, on December 6-8, 2019. Today, various types of intelligent system can be found everywhere. However, none of them can be developed or understood from only one perspective. As such, this book collects unique ways of thinking about intelligent systems. It discusses evolutionary and complex adaptive systems, which have been useful approaches for tackling intelligent systems. It also examines the recent developments in the field of artificial intelligence that are driving research on intelligent systems. Addressing topics related to intelligent transport systems, machine learning and neural networks, data science and decision analytics, evolutionary and nature-inspired computation, and agents and complex systems, this book is a valuable resource for researchers and practitioners wanting to develop or understand intelligent and evolutionary systems. .
(source: Nielsen Book Data)

• Agents and Complex Systems.- Data Mining and Its Applications.- Evolutionary Algorithms and Optimization.- Intelligent Systems and their Applications.- Nature Inspired Creative Computing.- Smart Workspace and Image Processing.
• (source: Nielsen Book Data)

This PALO volume constitutes the Proceedings of the 19th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2015), held in Bangkok, Thailand, November 22-25, 2015. The IES series of conference is an annual event that was initiated back in 1997 in Canberra, Australia. IES aims to bring together researchers from countries of the Asian Pacific Rim, in the fields of intelligent systems and evolutionary computation, to exchange ideas, present recent results and discuss possible collaborations. Researchers beyond Asian Pacific Rim countries are also welcome and encouraged to participate. The theme for IES 2015 is "Transforming Big Data into Knowledge and Technological Breakthroughs". The host organization for IES 2015 is the School of Information Technology (SIT), King Mongkut's University of Technology Thonburi (KMUTT), and it is technically sponsored by the International Neural Network Society (INNS). IES 2015 is collocated with three other conferences; namely, The 6th International Conference on Computational Systems-Biology and Bioinformatics 2015 (CSBio 2015), The 7th International Conference on Advances in Information Technology 2015 (IAIT 2015) and The 10th International Conference on e-Business 2015 (iNCEB 2015), as a major part of series of events to celebrate the SIT 20th anniversary and the KMUTT 55th anniversary.
(source: Nielsen Book Data)

• Sparse Bayesian ELM handling with missing data for multi-class classification.- A Fast Incremental Method Based on Regularized Extreme Learning Machine.- Parallel Ensemble of Online Sequential Extreme Learning Machine Based on MapReduce.- Explicit Computation of Input Weights in Extreme Learning Machines.- Subspace Detection on Concept Drifting Data Stream.- Inductive Bias for Semi-supervised Extreme Learning Machine.- ELM based Efficient Probabilistic Threshold Query on Uncertain Data.- Sample-based Extreme Learning Machine Regression with Absent Data.- Two Stages Query Processing Optimization based on ELM in the Cloud.- Domain Adaption Transfer Extreme Learning Machine.- Quasi-linear extreme learning machine model based nonlinear system identification.- A novel bio-inspired image recognition network with extreme learning machine.- A Deep and Stable Extreme Learning Approach for Classification and Regression.- Extreme Learning Machine Ensemble Classifier for Large-scale Data.- Pruned Extreme Learning Machine Optimization based on RANSAC Multi Model Response Regularization.- Learning ELM network weights using linear discriminant analysis.- An Algorithm for Classification over Uncertain Data based on Extreme Learning Machine.- Training Generalized Feedforward Kernelized Neural Networks on Very Large Datasets for Regression Using Minimal-Enclosing-Ball Approximation.- An Online Multiple Model Approach to Improve Performance in Univariate Time-Series Prediction.- A Self-organizing Mixture Extreme Leaning Machine for Time Series Forecasting.- A Robust AdaBoost.RT based Ensemble Extreme Learning Machine.- Machine learning reveals different brain activities during TOVA test.- Online Sequential Extreme Learning Machine with New Weight-setting Strategy or Non stationary Time Series Prediction.- RMSE-ELM: Recursive Model based Selective Ensemble of Extreme Learning Machines for Robustness Improvement.- Extreme Learning Machine for Regression and Classification Using L1-Norm and L2-Norm.- A Semi-supervised Online Sequential Extreme Learning Machine Method.- ELM feature mappings learning: Single-hidden-layer feed forward network without output weight.- ROS-ELM: A Robust Online Sequential Extreme Learning Machine for Big Data.- Deep Extreme Learning Machines for Classification.- C-ELM: A Curious Extreme Learning Machine for Classification Problems.- Review of Advances in Neural Networks: Neural Design Technology Stack.- Applying Regularization Least Squares Canonical Correction Analysis in Extreme Learning Machine formulti-label classification problems.- Least Squares Policy Iteration based on Random Vector Basis.- Identifying Indistinguishable Classes in Multi-class Classification Data Sets using ELM.- Effects of Training Datasets on both the Extreme Learning Machine and Support Vector Machine for Target Audience Identification on Twitter.- Extreme Learning Machine for Clustering.
• (source: Nielsen Book Data)

This book contains some selected papers from the International Conference on Extreme Learning Machine 2014, which was held in Singapore, December 8-10, 2014. This conference brought together the researchers and practitioners of Extreme Learning Machine (ELM) from a variety of fields to promote research and development of "learning without iterative tuning". The book covers theories, algorithms and applications of ELM. It gives the readers a glance of the most recent advances of ELM.
(source: Nielsen Book Data)

• Using Extreme Learning Machine for Filamentous Bulking Prediction and Forecast in Wastewater Treatment Plants
• Extreme Learning Machine for Linear Dynamical Systems Classification: Application to Human Activity Recognition
• Lens Distortion Correction Using ELM
• Pedestrian Detection in Thermal Infrared Image using Extreme Learning Machine
• Dynamic Texture Video Classification Using Extreme Learning Machine
• Uncertain XML Documents Classification Using Extreme Learning Machine
• Encrypted traffic identification based on randomness sparse feature and extreme learning machine
• Network Intrusion Detection Based on Extreme Learning Machine
• A Study on Three-dimensional Motion History Image and Extreme Learning Machine Oriented Body Movements Trajectory Recognition
• An Improved ELM Algorithm for the Measurement of Hot Metal Temperature in Blast Furnace
• Wi-Fi and Motion Sensors based Indoor Localization Combining ELM and Particle Filter
• Online Sequential Extreme Learning Machine for Watermarking
• Adaptive neural control of quadrotor helicopter with extreme learning machine
• Keyword Search on Probabilistic XML Data based on ELM
• A Novel HVS Based Gray Scale Image Watermarking Scheme Using Fast Fuzzy
• ELM Hybrid Architecture
• Wearable EyeGlass based Fall Detection using Weighted ELM
• Concise Feature Extraction based ELM for Active Service Quality Prediction
• Multi-class AdaBoost ELM and Its Application in LBP Based Face Recognition
• Detecting Copy Directions among Programs Using Extreme Learning Machines
• Extreme learning machine for reservoir parameter estimation in heterogeneous reservoir
• Multifault Diagnosis for Rolling Element Bearings Based on Extreme Learning Machine
• Gradient-based No-Reference Image Blur Assessment Using Extreme Learning Machine
• RFID Enabled Indoor Positioning for Real-time Manufacturing Execution System based on OS-ELM
• An Online Sequential Extreme Learning Machine for Tidal Prediction based on Improved Gath-Geva Fuzzy Segmentation
• Recognition of Human Stair Ascent and Descent Activities based on Extreme Learning Machine
• ELM Based Dynamic Modeling for Online Prediction of Content in Molten Iron
• Distributed Learning over Massive XML Documents in ELM Feature Space
• Hyperspectral Image Nonlinear Unmixing by Ensemble ELM Regression
• Text-Image Separation and Indexing in Historic Patent Document Image Based on Extreme Learning Machine
• Anomaly Detection with ELM-based Visual Attribute and Spatio-temporal Pyramid
• Modelling and Prediction of Surface Roughness and Power Consumption using Parallel Extreme Learning Machine based Particle Swarm Optimization
• OS-ELM based Emotion Recognition for Empathetic Elderly Companion
• Access Behavior Prediction in Distributed StorageSystem using Regularized Extreme Learning Machine
• ELM Based Fast CFD Model with Sensor Adjustment
• Melasma Image Segmentation Using Extreme Learning Machine
• Detection of Drivers' Distraction Using Semi-Supervised Extreme Learning Machine
• Driver Workload Detection in On-road Driving Environment using Machine Learning.

This book contains some selected papers from the International Conference on Extreme Learning Machine 2014, which was held in Singapore, December 8-10, 2014. This conference brought together the researchers and practitioners of Extreme Learning Machine (ELM) from a variety of fields to promote research and development of ℓ́ℓlearning without iterative tuningℓ́ℓ. ℗ℓThe book covers theories, algorithms and applications of ELM. It gives the readers a glance of the most recent advances of ELM. ℗ℓ.

• Test Pattern Modification to Minimize Test Power in Sequential Circuit
• A social network approach for improving job performance by promoting colleague intimacy
• Deep Learning Model for automated Image based Plant Disease Classification
• Opinion-based Machine Learning Approach for Fake News Classification
• Survey on IoMT: Amalgamation of Technologies-Wearable Body Sensor Network, Wearable Biosensors, ML and DL in IoMT.

The conference proceedings book is a collection of high-quality research articles in the field of intelligent vision and computing. It also serves as a forum for researchers and practitioners from both academia and industry to meet and share their expertise and experience. It provides opportunities for academicians and scientists along with professionals, policymakers, and practitioners from various fields in a global realm to present their research contributions and views, on one forum and interact with members inside and outside their own particular disciplines.

• Pretrained E-commerce Knowledge Graph Model for Product Classification.- A Novel Methodology for Object Detection in Highly Cluttered Images.- Extreme learning Machines for Offline Forged Signature Identification.- Randomized model structure selection approach for Extreme Learning Machine applied to Acid sulfate soils detection.- Online label distribution learning based on kernel extreme learning machine.
• (source: Nielsen Book Data)

This book contains papers from the International Conference on Extreme Learning Machine 2021, which was held in virtual on December 15-16, 2021. Extreme learning machines (ELM) aims to enable pervasive learning and pervasive intelligence. As advocated by ELM theories, it is exciting to see the convergence of machine learning and biological learning from the long-term point of view. ELM may be one of the fundamental `learning particles' filling the gaps between machine learning and biological learning (of which activation functions are even unknown). ELM represents a suite of (machine and biological) learning techniques in which hidden neurons need not be tuned: inherited from their ancestors or randomly generated. ELM learning theories show that effective learning algorithms can be derived based on randomly generated hidden neurons (biological neurons, artificial neurons, wavelets, Fourier series, etc.) as long as they are nonlinear piecewise continuous, independent of training data and application environments. Increasingly, evidence from neuroscience suggests that similar principles apply in biological learning systems. ELM theories and algorithms argue that "random hidden neurons" capture an essential aspect of biological learning mechanisms as well as the intuitive sense that the efficiency of biological learning need not rely on computing power of neurons. ELM theories thus hint at possible reasons why the brain is more intelligent and effective than current computers. This conference provides a forum for academics, researchers, and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique and brain learning. This book covers theories, algorithms, and applications of ELM. It gives readers a glance of the most recent advances of ELM.
(source: Nielsen Book Data)

• Handwritten Bengali Digit Classification using Deep Learning.- IOT Based COVID Patient Health Monitoring System In Quarantine.- Self-attention Convolution for Sparse to Dense Depth Completion.- Using Algorithm in Parametric Design as an Approach to Inspire Nature in Architectural Design.- Docker Container Orchestration Management in Cloud Computing.- Locally Weighted Mean Phase Angle (LWMPA) Based Tone Mapping Quality Index.
• (source: Nielsen Book Data)

This book gathers outstanding research papers presented at the International Conference on Intelligent Vision and Computing (ICIVC 2021), held online during October 03-04, 2021. ICIVC 2021 is organised by Sur University, Oman. The book presents novel contributions in intelligent vision and computing and serves as reference material for beginners and advanced research. The topics covered are intelligent systems, intelligent data analytics and computing, intelligent vision and applications collective intelligence, soft computing, optimization, cloud computing, machine learning, intelligent software, robotics, data science, data security, big data analytics, and signal natural language processing.
(source: Nielsen Book Data)

• Efficient Batch Parallel Online Sequential Extreme Learning Machine Algorithm Based on MapReduce.- Fixed-Point Evaluation of Extreme Learning Machine for Classification.- Multi-Layer Online Sequential Extreme Learning Machine for Image Classification.- ELM Meets Urban Computing: Ensemble Urban Data For Smart City Applications.- Local and Global Unsupervised Kernel Extreme Learning Machine and Its Application in Nonlinear Process Fault Detection.- Parallel Multi-Graph Classification Using Extreme Learning Machine and MapReduce.- Extreme Learning Machine for Large-Scale Graph Classification Based on MapReduce.- The Distance-based Representative Skyline Calculation using Unsupervised Extreme Learning Machines.- Multi-label Text Categorization Using L21-NormMinimization Extreme Learning Machine.- Cluster-based Outlier Detection Using Unsupervised Extreme Learning Machines.- Segmentation of the Left Ventricle in Cardiac MRI Using an ELM Model.- Channel Estimation Based on Extreme Learning Machine for High Speed Environments.- MIMO Modeling Based on Extreme Learning Machine.- Graph Classification based on Sparse Graph Feature Selection and Extreme Learning Machine.- Time Series Prediction Based on Online Sequential Improved Error Minimized Extreme Learning Machine.- Adaptive Input Shaping for Flexible Systems using an Extreme Learning Machine Algorithm Identification.- Kernel Based Semi-supervised Extreme Learning Machine and the Application in Traffic Congestion Evaluation.- Improvement of ELM Algorithm for Multi-Object Identification in Gesture Interaction.- SVM and ELM: Who Wins? Object Recognition with Deep Convolutional Features from ImageNet.- Learning with Similarity Functions: a Novel Design for the Extreme Learning Machine.- A Semi-Supervised Low Rank Kernel Learning Algorithm via Extreme Learning Machine.- Application of Extreme Learning Machine on Large Scale Traffic Congestion Prediction.- Extreme Learning Machine-Guided Collaborative Coding for Remote Sensing Image Classification.- Distributed Weighted Extreme Learning Machine for Big Imbalanced Data Learning.- NMR Image Segmentation based on Unsupervised Extreme Learning Machine.- Annotating Location Semantic Tags in LBSN Using Extreme Learning Machine.- Feature Extraction of Motor Imagery EEG based on Extreme Learning Machine Auto-Encoder.- Multimodal Fusion using Kernel-based ELM for Video Emotion Recognition.- Equality Constrained-Optimization-Based Semi-Supervised ELM for Modeling.- Signal Strength Temporal Variation in Indoor Location Estimation Extreme Learning Machine with Gaussian Kernel Based Relevance Feedback Scheme for Image Retrieval.- Routing Tree Maintenance based on Trajectory Prediction in Mobile Sensor Networks.- Two-Stage Hybrid Extreme Learning Machine for Sequential Imbalanced Data.- Feature Selection and Modelling of a Steam Turbine from a Combined Heat and Power Plant Using ELM.- On The Construction of Extreme Learning Machine for One Class Classifier.- Record Linkage for Event Identification in XML Feeds Stream Using ELM.- Timeliness Online Regularized Extreme Learning Machine.- An Efficient High-dimensional Big Data Storage Structure Based on US-ELM.- An Enhanced Extreme Learning Machine for Efficient Small Sample Classification.- Code Generation Technology of Digital Satellite.- ELM-based Velocity Inversion for Sandstone Reservoir in Yanqi Gas-Field.- Class-Constrained Extreme Learning Machine.
• (source: Nielsen Book Data)

This book contains some selected papers from the International Conference on Extreme Learning Machine 2015, which was held in Hangzhou, China, December 15-17, 2015. This conference brought together researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the Extreme Learning Machine (ELM) technique and brain learning. This book covers theories, algorithms ad applications of ELM. It gives readers a glance of the most recent advances of ELM.
(source: Nielsen Book Data)

• Large-Scale Scene Recognition based on Extreme Learning Machines
• Partially Connected ELM for Fast and Effective Scene Classification Optimization
• Two-Layer Extreme Learning Machine for Dimension Reduction
• Distributed Extreme Learning Machine with Alternating Direction Method of Multiplier
• An Adaptive Online Sequential Extreme Learning Machine for Real-Time Tidal Level Prediction
• Optimization of Outsourcing ELM problems in Cloud Computing from Multi-Parties
• H-MRST: A Novel Framework For Support Uncertain Data Range Query Using ELM
• The SVM-ELM Model based on Particle Swarm Optimization
• ELM-ML: Study on Multi-Label Classification using Extreme Learning Machine
• Sentiment Analysis of Chinese Micro Blog based on DNN and ELM and Vector Space Model
• Self Forward and Information Dissemination Prediction Research in SINA Microblog Using ELM
• Sparse Coding Extreme Learning Machine for Classification
• Continuous Top-K Remarkable comments Over Textual Streaming Data Using ELM
• ELM based Representational Learning for Fault Diagnosis of Wind Turbine Equipment
• Prediction of Pulp Concentration Using Extreme Learning Machine
• Rational and Self-Adaptive Evolutionary Extreme Learning Machine for Electricity Price Forecast
• Contractive ML-ELM for Invariance Robust Feature Extraction
• Automated Human Facial Expression Recognition Using Extreme Learning Machines
• Multi-Modal Deep Extreme Learning Machine for Robotic Grasping Recognition
• Denoising Deep Extreme Learning Machines for Sparse Representation
• Extreme Learning Machine based Point-of-Interest Recommendation in Location-based Social Networks
• The Granule-Based Interval Forecast for Wind Speed
• KELM : An Improved K-means Clustering Method using Extreme Learning Machine
• Wind Power Ramp Events Classification using Extreme Learning Machines
• Facial Expression Recognition Based on Ensemble Extreme Learning Machine with Eye Movements Information
• Correlation between Extreme Learning Machine and Entorhinal Hippocampal System
• RNA Secondary Structure Prediction using Extreme Learning Machine with Clustering Under-Sampling Technique
• Multi-Instance Multi-label learning by Extreme Learning Machine
• A Randomly Weighted Gabor Network for Visual-Thermal Infrared Face Recognition
• Dynamic Adjustment of Hidden Layer Structure for Convex Incremental Extreme Learning Machine
• ELMVIS+: Improved Nonlinear Visualization Technique using Cosine Distance and Extreme Learning Machines
• On Mutual Information over non-Euclidean Spaces, Data Mining and Data Privacy Levels
• Probabilistic Methods for Multiclass Classification Problems
• A Pruning Ensemble Model of Extreme Learning Machine with L1/2 Regularizer
• Evaluating Confidence Intervals for ELM Predictions
• Real-Time Driver Fatigue Detection Based on ELM
• A High Speed Multi-label Classifier based on Extreme Learning Machines
• Image Super-Resolution by PSOSEN of Local Receptive Fields Based Extreme Learning Machine
• Sparse Extreme Learning Machine for Regression
• WELM:Extreme Learning Machine with Wavelet Dynamic Co-Movement Analysis in High-Dimensional Time Series.

This book contains some selected papers from the International Conference on Extreme Learning Machine 2015, which was held in Hangzhou, China, December 15-17, 2015. This conference brought together researchers and engineers to share and exchange R & D experience on both theoretical studies and practical applications of the Extreme Learning Machine (ELM) technique and brain learning. This book covers theories, algorithms ad applications of ELM. It gives readers a glance of the most recent advances of ELM.