Skip to content
Register Sign in Wishlist

Kernel Methods and Machine Learning

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


Add to wishlist

Other available formats:

Looking for an evaluation copy?

This title is not currently available for evaluation. However, if you are interested in the title for your course we can consider offering an evaluation copy. To register your interest please contact providing details of the course you are teaching.

Product filter button
About the Authors
  • 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
    Read more

    Customer reviews

    Review was not posted due to profanity


    , create a review

    (If you're not , sign out)

    Please enter the right captcha value
    Please enter a star rating.
    Your review must be a minimum of 12 words.

    How do you rate this item?


    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: Available
  • 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

  • Resources for

    Kernel Methods and Machine Learning

    S. Y. Kung

    General Resources

    Lecturer Resources

    Find resources associated with this title

    Type Name Unlocked * Format Size

    Showing of

    Back to top

    *This title has one or more locked files and access is given only to lecturers adopting the textbook for their class. We need to enforce this strictly so that solutions are not made available to students. To gain access to locked resources you either need first to sign in or register for an account.

    These resources are provided free of charge by Cambridge University Press with permission of the author of the corresponding work, but are subject to copyright. You are permitted to view, print and download these resources for your own personal use only, provided any copyright lines on the resources are not removed or altered in any way. Any other use, including but not limited to distribution of the resources in modified form, or via electronic or other media, is strictly prohibited unless you have permission from the author of the corresponding work and provided you give appropriate acknowledgement of the source.

    If you are having problems accessing these resources please email

  • 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.

Sign In

Please sign in to access your account


Not already registered? Create an account now. ×

Sorry, this resource is locked

Please register or sign in to request access. If you are having problems accessing these resources please email

Register Sign in
Please note that this file is password protected. You will be asked to input your password on the next screen.

» Proceed

You are now leaving the Cambridge University Press website. Your eBook purchase and download will be completed by our partner Please see the permission section of the catalogue page for details of the print & copy limits on our eBooks.

Continue ×

Continue ×

Continue ×

Find content that relates to you

Join us online

This site uses cookies to improve your experience. Read more Close

Are you sure you want to delete your account?

This cannot be undone.


Thank you for your feedback which will help us improve our service.

If you requested a response, we will make sure to get back to you shortly.

Please fill in the required fields in your feedback submission.