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" Kernel methods and machine learning / "
S.Y. Kung (Princeton University).
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BL
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| Record Number
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620397
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dltt
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| Main Entry
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Kung, S. Y., (Sun Yuan)
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| Title & Author
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Kernel methods and machine learning /\ S.Y. Kung (Princeton University).
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| Page. NO
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xxiv, 591 pages :: illustrations ;; 26 cm
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| ISBN
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9781107024960
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: 110702496X
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| Bibliographies/Indexes
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Includes bibliographical references (pages 561-577) and index.
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| Contents
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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.
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| Abstract
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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"--
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Support vector machines.
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Machine learning.
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Kernel functions.
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COMPUTERS / Computer Vision Pattern Recognition.
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| NLM classification
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COM016000bisacsh
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