Cover Image

Kernel methods and machine learning / S.Y. Kung, Princeton University.

Show other versions (1)

"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 s...

Full description

Saved in:
Bibliographic Details
Main Author: Kung, S. Y. (Sun Yuan)
Format: Book
Language:English
Published: Cambridge ; New York : Cambridge University Press, 2014.
Subjects:
Support vector machines.
Machine learning.
Kernel functions.
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 and feature selection
  • 3. PCA and Kernel-PCA
  • 4. Feature selection
  • 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.

Similar Items