LEADER 06265nam 2200673 a 450 001 9910841883703321 005 20230725050917.0 010 $a1-283-17569-X 010 $a9786613175694 010 $a1-118-02559-8 010 $a1-118-02560-1 010 $a1-118-02558-X 035 $a(CKB)2550000000041598 035 $a(EBL)693187 035 $a(OCoLC)743204855 035 $a(SSID)ssj0000525579 035 $a(PQKBManifestationID)11318601 035 $a(PQKBTitleCode)TC0000525579 035 $a(PQKBWorkID)10507485 035 $a(PQKB)11051471 035 $a(MiAaPQ)EBC693187 035 $a(EXLCZ)992550000000041598 100 $a20101029d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aSelf-adaptive systems for machine intelligence$b[electronic resource] /$fHaibo He 210 $aHoboken, N.J. $cWiley-Interscience$d2011 215 $a1 online resource (248 p.) 300 $aDescription based upon print version of record. 311 $a0-470-34396-6 320 $aIncludes bibliographical references and index. 327 $aSELF-ADAPTIVE SYSTEMS FOR MACHINE INTELLIGENCE; CONTENTS; Preface; Acknowledgments; 1 Introduction; 1.1 The Machine Intelligence Research; 1.2 The Two-Fold Objectives: Data-Driven and Biologically Inspired Approaches; 1.3 How to Read This Book; 1.3.1 Part I: Data-Driven Approaches for Machine Intelligence (Chapters 2, 3, and 4); 1.3.2 Part II: Biologically-Inspired Approaches for Machine Intelligence (Chapters 4, 5, and 6); 1.4 Summary and Further Reading; References; 2 Incremental Learning; 2.1 Introduction; 2.2 Problem Foundation; 2.3 An Adaptive Incremental Learning Framework 327 $a2.4 Design of the Mapping Function2.4.1 Mapping Function Based on Euclidean Distance; 2.4.2 Mapping Function Based on Regression Learning Model; 2.4.3 Mapping Function Based on Online Value System; 2.4.3.1 A Three-Curve Fitting (TCF) Technique; 2.4.3.2 System-Level Architecture for Online Value Estimation; 2.5 Case Study; 2.5.1 Incremental Learning from Video Stream; 2.5.1.1 Feature Representation; 2.5.1.2 Experimental Results; 2.5.1.3 Concept Drifting Issue in Incremental Learning; 2.5.2 Incremental Learning for Spam E-mail Classification 327 $a2.5.2.1 Data Set Characteristic and System Configuration2.5.2.2 Simulation Results; 2.6 Summary; References; 3 Imbalanced Learning; 3.1 Introduction; 3.2 The Nature of Imbalanced Learning; 3.3 Solutions for Imbalanced Learning; 3.3.1 Sampling Methods for Imbalanced Learning; 3.3.1.1 Random Oversampling and Undersampling; 3.3.1.2 Informed Undersampling; 3.3.1.3 Synthetic Sampling with Data Generation; 3.3.1.4 Adaptive Synthetic Sampling; 3.3.1.5 Sampling with Data Cleaning Techniques; 3.3.1.6 Cluster-Based Sampling Method; 3.3.1.7 Integration of Sampling and Boosting 327 $a3.3.2 Cost-Sensitive Methods for Imbalanced Learning3.3.2.1 Cost-Sensitive Learning Framework; 3.3.2.2 Cost-Sensitive Data Space Weighting with Adaptive Boosting; 3.3.2.3 Cost-Sensitive Decision Trees; 3.3.2.4 Cost-Sensitive Neural Networks; 3.3.3 Kernel-Based Methods for Imbalanced Learning; 3.3.3.1 Kernel-Based Learning Framework; 3.3.3.2 Integration of Kernel Methods with Sampling Methods; 3.3.3.3 Kernel Modification Methods for Imbalanced Learning; 3.3.4 Active Learning Methods for Imbalanced Learning; 3.3.5 Additional Methods for Imbalanced Learning 327 $a3.4 Assessment Metrics for Imbalanced Learning3.4.1 Singular Assessment Metrics; 3.4.2 Receiver Operating Characteristics (ROC) Curves; 3.4.3 Precision-Recall (PR) Curves; 3.4.4 Cost Curves; 3.4.5 Assessment Metrics for Multiclass Imbalanced Learning; 3.5 Opportunities and Challenges; 3.6 Case Study; 3.6.1 Nonlinear Normalization; 3.6.2 Data Sets Distribution; 3.6.3 Simulation Results and Discussions; 3.7 Summary; References; 4 Ensemble Learning; 4.1 Introduction; 4.2 Hypothesis Diversity; 4.2.1 Q-Statistics; 4.2.2 Correlation Coefficient; 4.2.3 Disagreement Measure 327 $a4.2.4 Double-Fault Measure 330 $a"This book will advance the understanding and application of self-adaptive intelligent systems; therefore it will potentially benefit the long-term goal of replicating certain levels of brain-like intelligence in complex and networked engineering systems. It will provide new approaches for adaptive systems within uncertain environments. This will provide an opportunity to evaluate the strengths and weaknesses of the current state-of-the-art of knowledge, give rise to new research directions, and educate future professionals in this domain. Self-adaptive intelligent systems have wide applications from military security systems to civilian daily life. In this book, different application problems, including pattern recognition, classification, image recovery, and sequence learning, will be presented to show the capability of the proposed systems in learning, memory, and prediction. Therefore, this book will also provide potential new solutions to many real-world applications"--$cProvided by publisher. 330 $a"This book will advance the understanding and application of self-adaptive intelligent systems; therefore it will potentially benefit the long-term goal of replicating certain levels of brain-like intelligence in complex and networked engineering systems. It will provide new approaches for adaptive systems within uncertain environments. This will provide an opportunity to evaluate the strengths and weaknesses of the current state-of-the-art of knowledge, give rise to new research directions, and educate future professionals in this domain"--$cProvided by publisher. 606 $aMachine learning 606 $aSelf-organizing systems 606 $aArtificial intelligence 615 0$aMachine learning. 615 0$aSelf-organizing systems. 615 0$aArtificial intelligence. 676 $a006.3/1 676 $a006.31 686 $aCOM044000$2bisacsh 700 $aHe$b Haibo$f1976-$01730955 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910841883703321 996 $aSelf-adaptive systems for machine intelligence$94142855 997 $aUNINA