Kernel Methods and Machine Learning

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S. Y. KungCambridge University Press, 4/17/2014EAN 9781107024960, ISBN10: 110702496XHardcover, 572 pages, 25.2 x 17.6 x 2.9 cmLanguage: EnglishOffering a fundamental basis in kerne l-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.Part I. Machine Learning and Kernel Vector Spaces1. Fundamentals of machine learning2. Kernel-induced vector spacesPart II. Dimension-ReductionFeature Selection and PCA/KPCA3. Feature selection4. PCA and Kernel-PCAPart III. Unsupervised Learning Models for Cluster Analysis5. Unsupervised learning for cluster discovery6. Kernel methods for cluster discoveryPart IV. Kernel Ridge Regressors and Variants7. Kernel-based regression and regularization analysis8. Linear regression and discriminant analysis for supervised classification9. Kernel ridge regression for supervised classificationPart V. Support Vector Machines and Variants10. Support vector machines11. Support vector learning models for outlier detection12. Ridge-SVM learning modelsPart VI. Kernel Methods for Green Machine Learning Technologies13. Efficient kernel methods for learning and classifcationPart VII. Kernel Methods and Statistical Estimation Theory14. Statistical regression analysis and errors-in-variables models15Kernel methods for estimation, prediction, and system identificationPart VIII. AppendicesAppendix A. Validation and test of learning modelsAppendix B. kNN, PNN, and Bayes classifiersReferencesIndex. Show more