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• 1. Knowledge acquisition from examples usinmg maximal representation learning / S. Arun Kumar and S. Yegneshwar
• KBG: a knowledge based generalizer / G. Bisson
• Performance analysis of a probabalistic inductive learning system / K.C.C. Chan and A.K.C. Wong
• A comparative study of ID3 and backpropagation for English text-to-speech mapping / T.G. Dietterich, H. Hild, and G. Bakiri
• Learning from data with bounded inconsistency / H. Hirsh
• Conceptual set covering: improving fit-and-split algorithms / C.M. Kadie
• Incremental learning of rules and meta-rules / M. Schoenauer and M. Sebag
• An incremental method for finding multivariate splits for decision trees / P.E. Utgoff and C.E. Brodley
• Incremental induction of topologically minimal trees / W. Van de Velde.

• 2. A rational analysis of categorization / J.R. Anderson and M. Matessa
• Search control, utility, anf concept induction / B. Carlson, J. Weinberg, and D. Fisher
• Graph clustering and model learning by data compression / J. Segen
• 3. An analysis of representation shift in concept learning / W.E. Cohen
• Learning procedures by environment-driven constructive induction / D.V. Hume
• Beyond inversion of resolution / C. Rouveirol and J. Puget
• 4. Genetic programming / H. de Garis
• Improving the performance of genetic algorithms in automated discovery of parameters / N. Kadaba and K.E. Nygard
• Using genetic alogorithms to learn disjunctive rules from examples / R. Andrew McCallum and K.A. Spackman
• NEWBOOLE: a fast GBML system / P. Bonelli, A. Parodi, S. Sen and S. Wilson.

• 5. Learning functions in k-DNF from reinforcement / L.P. Kaelbling
• Is learning rate a good performance criterion for learning? / C. Sammut and J. Cribb
• Active perception and reinforcement learning / S.D. Whitehead and D.H. Ballard
• 6. Learning plans for competetive domains / S.L. Epstein
• Explanations of empirically derived reactive plans / D.F. Gordon and J.J. Grefenstette
• Learning and enforcement: stabilizing environments to facilitate activity / K.J. Hammond
• Simulation-assisted learning by competioion: effects of noise differences between training model and target environment / C.L. Ramsey, A.C. Schultz, and J.J. Grefenstette
• Integrated architecture for learning, planning, and reacting based on approximating dynamic programming / R.S. Sutton.

• 7. Reducing real-world failures of approximate explanation-based rules / S.W. Bennett
• Correcting and extending domain knowledge using outside guidance / J.E. Laird, M. Hucka, E.S. Yager, and C.M. Tuck
• Acquisition of dynamic control knowledge for a robotic manipulator / A.W. Moore
• Feature extraction and clustering of tactile impressions with connectionist models / M. Thint and P.P. Wang
• 8. Generalizing the order of goals as an approach to generalizing number / H. Bostrom
• Learning approximate control rules of high utility / W.W. Cohen
• Applying abstraction and simplification to learn in intratible domains / N.S. Fann
• Explanation-based learning with incomplete theories: a three-step approach / J. Genest, S. Matwin, and B. Plante
• Using abductive recovery of failed proofs for problem solving by analogy / Y. Kodratoff.

• Issues in the design of operator composition systems / S. Minton
• Incremental learning of explanation patterns and their indices / A. Ram
• 9. Integrated learning in a real domain / F. Bergadano, A. Giordana, L. Saitta, D. DeMarchi, and F. Brancadori
• Incremental version-space merging / H. Hirsch
• Average case analysis of conjunctive learning algorithms / M.J. Pazzani and W. Sarrett
• ILS: a framework for multi-paradigmatic learning / B. Silver, W. Frawley, G. Iba, J. Vittal, and K. Bradford
• An integrated framework of inducing rules from examples
• Y. WU, S. Wanf, and Q. Zhou
• 10. Adaptive parsing: a general method for learning idiosyncratic grammars / J.F. Lehman
• A comparison of learning techniques in second language learning / S.L. Lytinen and C.E. Moon
• Learning string patterns and tree patterns from examples
• K. Ko, A. Marron, and W. Tzeng.

• Learning with discrete multi-valued neurons / Z. Obradovic and I. Parberry
• 11. The general utility problems in machine learning / L.B. Holder
• A robust approach to numeric discovery / B. Nordhausen and P. Langley
• More results on the complexity of knowlegge base refinement: belief networks / M. Valtorta.

Papers from the June 1990 Conference held at the U. of Texas in Austin contain new work, new results, or major extensions of prior work in the field of machine learning. The topics include concept learning, machine discovery, connectionist learning, genetic algorithms, explanation-based learning and.
(source: Nielsen Book Data)

• Intelligent Systems and Machine Learning Applications in Health care
• Improving Multi-class Brain Tumor Detection using Vision Transformer as Feature Extractor
• Measles rash disease classification based on various CNN classifiers
• BRAIN IMAGING TOOL IN PATIENTS WITH TRANS ISCHEMIC ATTACK: A COMPARITIVE RESEARCH STUDY ANALYSIS OF COMPUTED TOMO
• EEG-based Stress Detection Using K-Means Clustering Method
• Detection of Psychological Stability Status Using Machine Learning Algorithms
• GLCM Based Feature Extraction and Medical X-ray Image Classification Using Machine Learning Techniques
• Multi filter wrapper enhanced machine learning model for cancer diagnosis
• An Interactive Web Solution for Electronic Health Records Segmentation and Prediction
• A Convolutional Neural Network based Prediction Model for Classification of Skin Cancer Images
• Multimodal Biomedical Image Fusion Techniques in Transform and spatial Domain: An Inclusive Survey
• Early Prediction of Coronary Heart Disease Using the Boruta Metho
• Design and Implementation of Obesity Healthcare System (OHS) using Flutter Platform
• A Survey on Covid-19 Knowledge Graphs and their Data Sources
• Identify Melanoma using CNN
• Digital Forensic & Network Security
• Machine Learning based Malware Analysis in Digital Forensic with IoT Devices
• Malicious Codes Detection : Deep Learning Techniques
• Securing Outsourced Personal Health Records On Cloud Using Encryption Techniques
• Design and Evaluation Decentralized Transactional Network Based Blockchain Technology using Omnet++
• Movie Synchronization System using Web Socket based Protocol
• A Study on Android Malware Detection using Machine Learning Algorithms
• Intelligent Communication Wireless Networks
• A Survey on Deep Recurrent Q networks
• Predicting Credit Card Defaults with Machine Learning Algorithm using Customer Database
• Enhancement of Signal to Noise Ratio for QAM signal in Noisy channel
• GSM ENABLED PATIENT MONITORING SYSTEM USING ARDUINO APPLICATION FOR CARDIAC SUPPORT
• Reso-Net: Generic Image Resolution Enhancement Using Convolutional Autoencoders
• Smart Traffic System with Green Time optimization using Fuzzy logic
• Early Diagnosis of Rheumatoid Arthritis of the Wrist Using Power Doppler Ultrasound: A Review
• An intrusion detection System and Attack Intension used in Network forensic exploration
• Comparison of Advanced Encryption Standard Variants Targeted at FPGA Architectures
• Characteristics and Analysis of ElectroGastroGram Signal
• A COMPARATIVE STUDY OF POWER OPTIMIZATION USING LEAKAGE REDUCTION TECHNIQUES
• Design and Implementation of 4-bit High Speed Array Multiplier for Image CodingDesign and Implementation of 4-bit High Speed Array Multiplier for Image Coding
• Internet of Things (IoT) Applications
• Smart Traffic Police Helmet: Using Image Processing and IoT
• Interconnected Hospitals using IOT
• Automatic oxygen ventilation and monitoring system using IoT
• Social Informatics
• Social Media Sentiment Analysis Using Deep Learning Approach
• Movie Recommendation Using Content-Based and Collaborative Filtering Approach
• Movie Recommendation Using Content-Based and Collaborative Filtering Approach
• Movie Recommendation System using Composite Ranking
• SOCIAL DISTANCING AND FACE MASK DETECTION USING OPEN CV
• Predicting the Likeliest Customers; Minimizing Losses on Product Trials using Business Analytics
• Television price prediction based on features with Machine Learning
• Emerging Applications
• A Model for Engineering, Procurement, and Construction (EPC) Organizations Using Vendor Performance Rating System
• F2PMSMD: Design of a Fusion model to identify Fake Profiles from Multimodal Social Media Datasets
• A Novel Model To Predict The Whack Of pandemics On The International Rankings Of Academia
• Mining Ancient Medicine Texts Towards an Ontology of Remedies – A Semi-automatic Approach
• A Blockchain Enabled Medical Tourism Ecosystem
• Measuring the impact of oil revenues on government debt in selected countries by using ARDL Model
• DIAGNOSIS OF PLANT DISEASES BY IMAGE PROCESSING MODEL FOR SUSTAINABLE SOLUTIONS
• Face Mask Detection: An Application of Artificial Intelligence
• A critical review of Faults in cloud computing: Types, Detection, and Mitigation schemes
• VIDEO CONTENT ANALYSIS USING DEEP LEARNING METHODS
• Prediction of Cochlear Disorders using face tilt estimation and Audiology dataset
• Quantum Data Management and Quantum Machine Learning for Data Management: State-of-the-Art and Open Challenges
• Multivariate Analysis and Comparison of Machine Learning Algorithms: A case study of Cereals of America
• Competitive Programming Vestige using Machine Learning
• Machine Learning Techniques for Aspect Analysis of Employee Attrition
• AI-Enabled Automation Solution for Utilization Management in Healthcare Insurance
• Real-Time Identification of Medical Equipment using Deep CNN And Computer Vision
• Design of a intelligent crutch tool for elders
• An approach to new technical solutions in resource allocation based on artificial intelligence
• Gesture Controlled Power Window Using Deep Learning
• Novel Deep Learning techniques to design the model and Predict Facial Expression, gender, and age recognition
• A Comprehensive Review on Various Data Science Technologies used for Enhancing the Quality of Education Systems
• AI/ML Based Sensitive Data Discovery and Classification of Unstructured Data Sources
• AI/ML Based Sensitive Data Discovery and Classification of Unstructured Data Sources
• Machine Learning Based Spectrum sensing for Secure data Transmission using Cuckoo search optimization

This two-volume set constitutes the refereed proceedings of the First EAI International Conference on Intelligent Systems and Machine Learning, ICISML 2022, held in Hyderabad, India, in December 16-17,2022. The 75 full papers presented were carefully reviewed and selected from 209 submissions. The conference focuses on Intelligent Systems and Machine Learning Applications in Health care; Digital Forensic & Network Security; Intelligent Communication Wireless Networks; Internet of Things (IoT) Applications; Social Informatics; and Emerging Applications

• Intelligent Systems and Machine Learning Applications in Health care
• Improving Multi-class Brain Tumor Detection using Vision Transformer as Feature Extractor
• Measles rash disease classification based on various CNN classifiers
• BRAIN IMAGING TOOL IN PATIENTS WITH TRANS ISCHEMIC ATTACK: A COMPARITIVE RESEARCH STUDY ANALYSIS OF COMPUTED TOMO
• EEG-based Stress Detection Using K-Means Clustering Method
• Detection of Psychological Stability Status Using Machine Learning Algorithms
• GLCM Based Feature Extraction and Medical X-ray Image Classification Using Machine Learning Techniques
• Multi filter wrapper enhanced machine learning model for cancer diagnosis
• An Interactive Web Solution for Electronic Health Records Segmentation and Prediction
• A Convolutional Neural Network based Prediction Model for Classification of Skin Cancer Images
• Multimodal Biomedical Image Fusion Techniques in Transform and spatial Domain: An Inclusive Survey
• Early Prediction of Coronary Heart Disease Using the Boruta Metho
• Design and Implementation of Obesity Healthcare System (OHS) using Flutter Platform
• A Survey on Covid-19 Knowledge Graphs and their Data Sources
• Identify Melanoma using CNN
• Digital Forensic & Network Security
• Machine Learning based Malware Analysis in Digital Forensic with IoT Devices
• Malicious Codes Detection : Deep Learning Techniques
• Securing Outsourced Personal Health Records On Cloud Using Encryption Techniques
• Design and Evaluation Decentralized Transactional Network Based Blockchain Technology using Omnet++
• Movie Synchronization System using Web Socket based Protocol
• A Study on Android Malware Detection using Machine Learning Algorithms
• Intelligent Communication Wireless Networks
• A Survey on Deep Recurrent Q networks
• Predicting Credit Card Defaults with Machine Learning Algorithm using Customer Database
• Enhancement of Signal to Noise Ratio for QAM signal in Noisy channel
• GSM ENABLED PATIENT MONITORING SYSTEM USING ARDUINO APPLICATION FOR CARDIAC SUPPORT
• Reso-Net: Generic Image Resolution Enhancement Using Convolutional Autoencoders
• Smart Traffic System with Green Time optimization using Fuzzy logic
• Early Diagnosis of Rheumatoid Arthritis of the Wrist Using Power Doppler Ultrasound: A Review
• An intrusion detection System and Attack Intension used in Network forensic exploration
• Comparison of Advanced Encryption Standard Variants Targeted at FPGA Architectures
• Characteristics and Analysis of ElectroGastroGram Signal
• A COMPARATIVE STUDY OF POWER OPTIMIZATION USING LEAKAGE REDUCTION TECHNIQUES
• Design and Implementation of 4-bit High Speed Array Multiplier for Image CodingDesign and Implementation of 4-bit High Speed Array Multiplier for Image Coding
• Internet of Things (IoT) Applications
• Smart Traffic Police Helmet: Using Image Processing and IoT
• Interconnected Hospitals using IOT
• Automatic oxygen ventilation and monitoring system using IoT
• Social Informatics
• Social Media Sentiment Analysis Using Deep Learning Approach
• Movie Recommendation Using Content-Based and Collaborative Filtering Approach
• Movie Recommendation Using Content-Based and Collaborative Filtering Approach
• Movie Recommendation System using Composite Ranking
• SOCIAL DISTANCING AND FACE MASK DETECTION USING OPEN CV
• Predicting the Likeliest Customers; Minimizing Losses on Product Trials using Business Analytics
• Television price prediction based on features with Machine Learning
• Emerging Applications
• A Model for Engineering, Procurement, and Construction (EPC) Organizations Using Vendor Performance Rating System
• F2PMSMD: Design of a Fusion model to identify Fake Profiles from Multimodal Social Media Datasets
• A Novel Model To Predict The Whack Of pandemics On The International Rankings Of Academia
• Mining Ancient Medicine Texts Towards an Ontology of Remedies – A Semi-automatic Approach
• A Blockchain Enabled Medical Tourism Ecosystem
• Measuring the impact of oil revenues on government debt in selected countries by using ARDL Model
• DIAGNOSIS OF PLANT DISEASES BY IMAGE PROCESSING MODEL FOR SUSTAINABLE SOLUTIONS
• Face Mask Detection: An Application of Artificial Intelligence
• A critical review of Faults in cloud computing: Types, Detection, and Mitigation schemes
• VIDEO CONTENT ANALYSIS USING DEEP LEARNING METHODS
• Prediction of Cochlear Disorders using face tilt estimation and Audiology dataset
• Quantum Data Management and Quantum Machine Learning for Data Management: State-of-the-Art and Open Challenges
• Multivariate Analysis and Comparison of Machine Learning Algorithms: A case study of Cereals of America
• Competitive Programming Vestige using Machine Learning
• Machine Learning Techniques for Aspect Analysis of Employee Attrition
• AI-Enabled Automation Solution for Utilization Management in Healthcare Insurance
• Real-Time Identification of Medical Equipment using Deep CNN And Computer Vision
• Design of a intelligent crutch tool for elders
• An approach to new technical solutions in resource allocation based on artificial intelligence
• Gesture Controlled Power Window Using Deep Learning
• Novel Deep Learning techniques to design the model and Predict Facial Expression, gender, and age recognition
• A Comprehensive Review on Various Data Science Technologies used for Enhancing the Quality of Education Systems
• AI/ML Based Sensitive Data Discovery and Classification of Unstructured Data Sources
• AI/ML Based Sensitive Data Discovery and Classification of Unstructured Data Sources
• Machine Learning Based Spectrum sensing for Secure data Transmission using Cuckoo search optimization

This two-volume set constitutes the refereed proceedings of the First EAI International Conference on Intelligent Systems and Machine Learning, ICISML 2022, held in Hyderabad, India, in December 16-17,2022. The 75 full papers presented were carefully reviewed and selected from 209 submissions. The conference focuses on Intelligent Systems and Machine Learning Applications in Health care; Digital Forensic & Network Security; Intelligent Communication Wireless Networks; Internet of Things (IoT) Applications; Social Informatics; and Emerging Applications

• Workshop on Data Science for Social Good (SoGood 2022)
• Preface from the workshop organisers
• Gender Stereotyping Impact on Facial Expression Recognition
• A Social Media Tool for Domain-Specific Information Retrieval - A Case Study in Human Trafficking
• A Unified Framework for Assessing Energy Efficiency of Machine Learning
• Fault Detection in Wastewater Treatment Plants: Application of Autoencoders Models with Streaming Data
• A Temporal Fusion Transformer for Long-term Explainable Prediction of Emergency Department Overcrowding
• Exploitation and Merge of Information Sources for Public Procurement Improvement
• Geovisualisation tools for reporting and monitoring Transthyretin-associated Familial Amyloid Polyneuropathy disease
• Evaluation of Group Fairness Measures in Student Performance Prediction Problems
• Combining Image Enhancement Techniques and Deep Learning for Shallow Water Benthic Marine Litter Detection
• Ethical and Technological AI Risks Classification: A Human vs Machine Approach
• A Reinforcement Learning Algorithm for Fair Electoral Redistricting in Parliamentary Systems
• Study on Correlation Between Vehicle Emissions and Air Quality in Porto
• Intelligently Detecting Information Online-weaponisation Trends (IDIOT)
• Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2022)
• Preface from the workshop organisers
• Multi-Modal Terminology Management: Corpora, Data Models and Implementations in TermSTAR
• Cluster algorithm for social choice
• Sentimental Analysis of COVID-19 Vaccine Tweets using BERT+NBSVM
• Rules, subgroups and redescriptions as features in classification tasks
• Bitpaths: compressing datasets without decreasing predictive performance
• Anomaly Detection for Physical Threat Intelligence
• Workshop on eXplainable Knowledge Discovery in Data Mining (XKDD 2022)
• Preface from the workshop organisers
• Is Attention Interpretation? A Quantitative Assessment on Sets
• From Disentangled Representation to Concept Ranking: Interpreting Deep Representations in Image Classification tasks
• RangeGrad: Explaining Neural Networks by Measuring Uncertainty through Bound Propagation
• An Empirical Evaluation of Predicted Outcomes as Explanations in Human-AI Decision-Making
• Local Multi-Label Explanations for Random Forest
• Interpretable and Reliable Rule Classification based on Conformal Prediction
• Measuring the Burden of (Un)fairness Using Counterfactuals
• Are SHAP values biased towards high-entropy features?
• Simple explanations to summarise Subgroup Discovery outcomes: a case study concerning patient phenotyping
• Limits of XAI task performance evaluation: an e-sport prediction example
• Improving the quality of rule-based GNN explanations
• Exposing Racial Dialect Bias in Abusive Language Detection: Can Explainability Play a Role?
• On the Granularity of Explanations in Model Agnostic NLP Interpretability
• Workshop on Uplift Modeling (UMOD 2022)
• Preface from the workshop organisers
• Estimating the impact of coupon non-usage
• Shrinkage estimators for uplift regression
• Workshop on IoT, Edge and Mobile for Embedded Machine Learning (ITEM 2022)
• Preface from the workshop organisers
• Hierarchical Design Space Exploration for Distributed CNN Inference at the Edge
• Automated Search for Deep Neural Network Inference Partitioning on Embedded FPGA
• Framework to Evaluate Deep Learning Algorithms for Edge Inference and Training
• Hardware Execution Time Prediction for Neural Network Layers
• Enhancing Energy-eiciency by Solving the Throughput Bottleneck of LSTM Cells for Embedded FPGAs
• Accelerating RNN-based Speech Enhancement on a Multi-Core MCU with Mixed FP16-INT8 Post-Training Quantization
• LDRNet: Enabling Real-time Document Localization on Mobile Devices.

This volume constitutes the papers of several workshops which were held in conjunction with the International Workshops of ECML PKDD 2022 on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022, held in Grenoble, France, during September 1923, 2022. The 73 revised full papers and 6 short papers presented in this book were carefully reviewed and selected from 143 submissions. ECML PKDD 2022 presents the following workshops: Workshop on Data Science for Social Good (SoGood 2022) Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2022) Workshop on Explainable Knowledge Discovery in Data Mining (XKDD 2022) Workshop on Uplift Modeling (UMOD 2022) Workshop on IoT, Edge and Mobile for Embedded Machine Learning (ITEM 2022) Workshop on Mining Data for Financial Application (MIDAS 2022) Workshop on Machine Learning for Cybersecurity (MLCS 2022) Workshop on Machine Learning for Buildings Energy Management (MLBEM 2022) Workshop on Machine Learning for Pharma and Healthcare Applications (PharML 2022) Workshop on Data Analysis in Life Science (DALS 2022) Workshop on IoT Streams for Predictive Maintenance (IoT-PdM 2022).

• Proceedings of Active Conceptual Modeling-Learning (ACM-L) Workshop, November 8, 2006, Tucson, Arizona, USA.- Overview of Papers in 2006 Active Conceptual Modeling of Learning (ACM-L) Workshop.- Architecture for Active Conceptual Modeling of Learning.- Understanding the Semantics of Data Provenance to Support Active Conceptual Modeling.- Adaptive and Context-Aware Reconciliation of Reactive and Pro-active Behavior in Evolving Systems.- A Common Core for Active Conceptual Modeling for Learning from Surprises.- Actively Evolving Conceptual Models for Mini-World and Run-Time Environment Changes.- Achievements and Problems of Conceptual Modelling.- Metaphor Modeling on the Semantic Web.- Schema Changes and Historical Information in Conceptual Models in Support of Adaptive Systems.- Using Active Modeling in Counterterrorism.- To Support Emergency Management by Using Active Modeling: A Case of Hurricane Katrina.- Using Ontological Modeling in a Context-Aware Summarization System to Adapt Text for Mobile Devices.- Accommodating Streams to Support Active Conceptual Modeling of Learning from Surprises.- Invited Paper.- Approaches to the Active Conceptual Modelling of Learning.- Spatio-temporal and Multi-representation Modeling: A Contribution to Active Conceptual Modeling.- Postponing Schema Definition: Low Instance-to-Entity Ratio (LItER) Modelling.- Research Issues in Active Conceptual Modeling of Learning: Summary of Panel Discussions in Two Workshops (May 2006) and (November 2006).
• (source: Nielsen Book Data)

This volume is a collection of papers presented during the first International ACM-L Workshop, which was held in Tucson, Arizona, during the 25th International Conference on Conceptual Modeling, ER 2006. Included in this state-of-the-art survey are 11 revised full papers, carefully reviewed and selected from the workshop presentations. These are rounded off with four invited lectures and an introductory overview, and represent the current thinking in conceptual modeling research.
(source: Nielsen Book Data)

• Proceedings of Active Conceptual Modeling-Learning (ACM-L) Workshop, November 8, 2006, Tucson, Arizona, USA.- Overview of Papers in 2006 Active Conceptual Modeling of Learning (ACM-L) Workshop.- Architecture for Active Conceptual Modeling of Learning.- Understanding the Semantics of Data Provenance to Support Active Conceptual Modeling.- Adaptive and Context-Aware Reconciliation of Reactive and Pro-active Behavior in Evolving Systems.- A Common Core for Active Conceptual Modeling for Learning from Surprises.- Actively Evolving Conceptual Models for Mini-World and Run-Time Environment Changes.- Achievements and Problems of Conceptual Modelling.- Metaphor Modeling on the Semantic Web.- Schema Changes and Historical Information in Conceptual Models in Support of Adaptive Systems.- Using Active Modeling in Counterterrorism.- To Support Emergency Management by Using Active Modeling: A Case of Hurricane Katrina.- Using Ontological Modeling in a Context-Aware Summarization System to Adapt Text for Mobile Devices.- Accommodating Streams to Support Active Conceptual Modeling of Learning from Surprises.- Invited Paper.- Approaches to the Active Conceptual Modelling of Learning.- Spatio-temporal and Multi-representation Modeling: A Contribution to Active Conceptual Modeling.- Postponing Schema Definition: Low Instance-to-Entity Ratio (LItER) Modelling.- Research Issues in Active Conceptual Modeling of Learning: Summary of Panel Discussions in Two Workshops (May 2006) and (November 2006).
• (source: Nielsen Book Data)

This volume is a collection of papers presented during the first International ACM-L Workshop, which was held in Tucson, Arizona, during the 25th International Conference on Conceptual Modeling, ER 2006. Included in this state-of-the-art survey are 11 revised full papers, carefully reviewed and selected from the workshop presentations. These are rounded off with four invited lectures and an introductory overview, and represent the current thinking in conceptual modeling research.
(source: Nielsen Book Data)

• Invited Presentations.- Property Testing: A Learning Theory Perspective.- Spectral Algorithms for Learning and Clustering.- Unsupervised, Semisupervised and Active Learning I.- Minimax Bounds for Active Learning.- Stability of k-Means Clustering.- Margin Based Active Learning.- Unsupervised, Semisupervised and Active Learning II.- Learning Large-Alphabet and Analog Circuits with Value Injection Queries.- Teaching Dimension and the Complexity of Active Learning.- Multi-view Regression Via Canonical Correlation Analysis.- Statistical Learning Theory.- Aggregation by Exponential Weighting and Sharp Oracle Inequalities.- Occam's Hammer.- Resampling-Based Confidence Regions and Multiple Tests for a Correlated Random Vector.- Suboptimality of Penalized Empirical Risk Minimization in Classification.- Transductive Rademacher Complexity and Its Applications.- Inductive Inference.- U-Shaped, Iterative, and Iterative-with-Counter Learning.- Mind Change Optimal Learning of Bayes Net Structure.- Learning Correction Grammars.- Mitotic Classes.- Online and Reinforcement Learning I.- Regret to the Best vs. Regret to the Average.- Strategies for Prediction Under Imperfect Monitoring.- Bounded Parameter Markov Decision Processes with Average Reward Criterion.- Online and Reinforcement Learning II.- On-Line Estimation with the Multivariate Gaussian Distribution.- Generalised Entropy and Asymptotic Complexities of Languages.- Q-Learning with Linear Function Approximation.- Regularized Learning, Kernel Methods, SVM.- How Good Is a Kernel When Used as a Similarity Measure?.- Gaps in Support Vector Optimization.- Learning Languages with Rational Kernels.- Generalized SMO-Style Decomposition Algorithms.- Learning Algorithms and Limitations on Learning.- Learning Nested Halfspaces and Uphill Decision Trees.- An Efficient Re-scaled Perceptron Algorithm for Conic Systems.- A Lower Bound for Agnostically Learning Disjunctions.- Sketching Information Divergences.- Competing with Stationary Prediction Strategies.- Online and Reinforcement Learning III.- Improved Rates for the Stochastic Continuum-Armed Bandit Problem.- Learning Permutations with Exponential Weights.- Online and Reinforcement Learning IV.- Multitask Learning with Expert Advice.- Online Learning with Prior Knowledge.- Dimensionality Reduction.- Nonlinear Estimators and Tail Bounds for Dimension Reduction in l 1 Using Cauchy Random Projections.- Sparse Density Estimation with ?1 Penalties.- ?1 Regularization in Infinite Dimensional Feature Spaces.- Prediction by Categorical Features: Generalization Properties and Application to Feature Ranking.- Other Approaches.- Observational Learning in Random Networks.- The Loss Rank Principle for Model Selection.- Robust Reductions from Ranking to Classification.- Open Problems.- Rademacher Margin Complexity.- Open Problems in Efficient Semi-supervised PAC Learning.- Resource-Bounded Information Gathering for Correlation Clustering.- Are There Local Maxima in the Infinite-Sample Likelihood of Gaussian Mixture Estimation?.- When Is There a Free Matrix Lunch?.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 20th Annual Conference on Learning Theory, COLT 2007, held in San Diego, CA, USA in June 2007. It covers unsupervised, semisupervised and active learning, statistical learning theory, inductive inference, regularized learning, kernel methods, SVM, online and reinforcement learning, learning algorithms and limitations on learning, dimensionality reduction, as well as open problems.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 19th Annual Conference on Learning Theory, COLT 2006, held in Pittsburgh, Pennsylvania, USA in June 2006. The 43 revised full papers presented together with 2 articles on open problems and 3 invited lectures were carefully reviewed and selected from a total of 102 submissions. The papers cover a wide range of topics including clustering, un- and semisupervised learning, statistical learning theory, regularized learning and kernel methods, query learning and teaching, inductive inference, learning algorithms and limitations on learning, online aggregation, online prediction and reinforcement learning.
(source: Nielsen Book Data)

This book constitutes the thoroughly refereed post-proceedings of the First PASCAL (pattern analysis, statistical modelling and computational learning) Machine Learning Challenges Workshop, MLCW 2005, held in Southampton, UK in April 2005. The 25 revised full papers presented were carefully selected during two rounds of reviewing and improvement from about 50 submissions. The papers reflect the concepts of three challenges dealt with in the workshop: finding an assessment base on the uncertainty of predictions using classical statistics, Bayesian inference, and statistical learning theory; the second challenge was to recognize objects from a number of visual object classes in realistic scenes; the third challenge of recognizing textual entailment addresses semantic analysis of language to form a generic framework for applied semantic inference in text understanding.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 17th European Conference on Machine Learning, ECML 2006, held in Berlin, Germany in September 2006, jointly with PKDD 2006. The 46 revised full papers and 36 revised short papers presented together with abstracts of 5 invited talks were carefully reviewed and selected from 564 papers submitted to both, ECML and PKDD. The papers present a wealth of new results in the area and address all current issues in machine learning.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 16th European Conference on Machine Learning, ECML 2005, jointly held with PKDD 2005 in Porto, Portugal, in October 2005. The 40 revised full papers and 32 revised short papers presented together with abstracts of 6 invited talks were carefully reviewed and selected from 335 papers submitted to ECML and 30 papers submitted to both, ECML and PKDD. The papers present a wealth of new results in the area and address all current issues in machine learning.
(source: Nielsen Book Data)

Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tubingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 15th European Conference on Machine Learning, ECML 2004, held in Pisa, Italy, in September 2004, jointly with PKDD 2004. The 45 revised full papers and 6 revised short papers presented together with abstracts of 5 invited talks were carefully reviewed and selected from 280 papers submitted to ECML and 107 papers submitted to both, ECML and PKDD. The papers present a wealth of new results in the area and address all current issues in machine learning.
(source: Nielsen Book Data)

This book presents revised reviewed versions of lectures given during the Machine Learning Summer School held in Canberra, Australia, in February 2002. The lectures address the following key topics in algorithmic learning: statistical learning theory, kernel methods, boosting, reinforcement learning, theory learning, association rule learning, and learning linear classifier systems. Thus, the book is well balanced between classical topics and new approaches in machine learning. Advanced students and lecturers will find this book a coherent in-depth overview of this exciting area, while researchers will use this book as a valuable source of reference.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 5th International Workshop on Learning Classifier Systems, IWLCS 2003, held in Granada, Spain in September 2003 in conjunction with PPSN VII.The 10 revised full papers presented together with a comprehensive bibliography on learning classifier systems were carefully reviewed and selected during two rounds of refereeing and improvement. All relevant issues in the area are addressed.
(source: Nielsen Book Data)