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Record Nr. |
UNINA9910830087803321 |
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Autore |
He Haibo <1976-> |
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Titolo |
Self-adaptive systems for machine intelligence [[electronic resource] /] / Haibo He |
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Pubbl/distr/stampa |
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Hoboken, N.J., : Wiley-Interscience, 2011 |
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ISBN |
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1-283-17569-X |
9786613175694 |
1-118-02559-8 |
1-118-02560-1 |
1-118-02558-X |
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Descrizione fisica |
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1 online resource (248 p.) |
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Classificazione |
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Disciplina |
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Soggetti |
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Machine learning |
Self-organizing systems |
Artificial intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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SELF-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 |
2.4 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 |
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Issue in Incremental Learning; 2.5.2 Incremental Learning for Spam E-mail Classification |
2.5.2.1 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 |
3.3.2 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 |
3.4 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 |
4.2.4 Double-Fault Measure |
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Sommario/riassunto |
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"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"-- |
"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"-- |
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