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Kernel Methods and Machine Learning

£72.99

  • Author: S. Y. Kung, Princeton University, New Jersey
  • Date Published: April 2014
  • availability: In stock
  • format: Hardback
  • isbn: 9781107024960

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  • Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors.

    • Covers various cutting edge techniques that can be used as a practical and accessible solution for a broad spectrum of application domains
    • Discusses computationally efficient techniques suitable for green-IT technologies
    • Explains the theory in an accessible, step-by-step manner, with problems and examples encouraging the reader to apply the theory in practice
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    Product details

    • Date Published: April 2014
    • format: Hardback
    • isbn: 9781107024960
    • length: 572 pages
    • dimensions: 252 x 176 x 29 mm
    • weight: 1.35kg
    • contains: 136 b/w illus. 21 tables
    • availability: In stock
  • Table of Contents

    Part I. Machine Learning and Kernel Vector Spaces:
    1. Fundamentals of machine learning
    2. Kernel-induced vector spaces
    Part II. Dimension-Reduction: Feature Selection and PCA/KPCA:
    3. Feature selection
    4. PCA and Kernel-PCA
    Part III. Unsupervised Learning Models for Cluster Analysis:
    5. Unsupervised learning for cluster discovery
    6. Kernel methods for cluster discovery
    Part IV. Kernel Ridge Regressors and Variants:
    7. Kernel-based regression and regularization analysis
    8. Linear regression and discriminant analysis for supervised classification
    9. Kernel ridge regression for supervised classification
    Part V. Support Vector Machines and Variants:
    10. Support vector machines
    11. Support vector learning models for outlier detection
    12. Ridge-SVM learning models
    Part VI. Kernel Methods for Green Machine Learning Technologies:
    13. Efficient kernel methods for learning and classifcation
    Part VII. Kernel Methods and Statistical Estimation Theory:
    14. Statistical regression analysis and errors-in-variables models
    15: Kernel methods for estimation, prediction, and system identification
    Part VIII. Appendices: Appendix A. Validation and test of learning models
    Appendix B. kNN, PNN, and Bayes classifiers
    References
    Index.

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    Kernel Methods and Machine Learning

    S. Y. Kung

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  • Author

    S. Y. Kung, Princeton University, New Jersey
    S. Y. Kung is a Professor in the Department of Electrical Engineering at Princeton University. His research areas include VLSI array/parallel processors, system modeling and identification, wireless communication, statistical signal processing, multimedia processing, sensor networks, bioinformatics, data mining and machine learning.

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