紀錄類型 : 書目-語言資料,印刷品: 單行本
作者 : KungS. Y.,
出版地 : Cambridge
出版者 : Cambridge University Press;
出版年 : 2014.
面頁冊數 : xxiv, 591 p.ill. : 26 cm.;
標題 : Kernel functions. -
標題 : Machine learning. -
標題 : Support vector machines. -
ISBN : 110702496X
ISBN : 9781107024960
內容註 : Machine generated contents note: 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.
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200 1 $aKernel methods and machine learning$fS.Y. Kung.
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215 1 $axxiv, 591 p.$cill.$d26 cm.
320 $aIncludes bibliographical references (p. 561-577) and index.
327 1 $aMachine generated contents note: 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.
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606 $aMachine learning.$2lc$392546
606 $aSupport vector machines.$2lc$3233597
676 $a006.3/10151252$v23
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700 1$aKung$bS. Y.$g(Sun Yuan)$3233881