Kernel methods and machine learning[...
Kung, S. Y.

 

  • Kernel methods and machine learning[electronic resource] /
  • 紀錄類型: 書目-電子資源 : Monograph/item
    杜威分類號: 006.310151252
    書名/作者: Kernel methods and machine learning/ S.Y. Kung.
    其他題名: Kernel Methods & Machine Learning
    作者: Kung, S. Y.
    出版者: Cambridge : : Cambridge University Press,, 2014.
    面頁冊數: xxiv, 591 p. : : ill., digital ;; 24 cm.
    標題: Support vector machines.
    標題: Machine learning.
    標題: Kernel functions.
    ISBN: 9781139176224
    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.
    摘要、提要註: 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.
    電子資源: https://doi.org/10.1017/CBO9781139176224
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