Kernel Methods and Machine Learning

ISBN: 9781139698870 出版年：2014 页码：618 S Y Kung Cambridge University Press

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.