عنوان

Kernel methods and machine learning /

(Number (ISBN

110702496X

(Number (ISBN

9781107024960

Number

dltt

Title Proper

Kernel methods and machine learning /

General Material Designation

[Book]

First Statement of Responsibility

S.Y. Kung (Princeton University).

Specific Material Designation and Extent of Item

xxiv, 591 pages :

Other Physical Details

illustrations ;

Dimensions

26 cm

Text of Note

Includes bibliographical references (pages 561-577) and index.

Text of 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 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.

0

Text of Note

"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"--

Kernel functions.

Machine learning.

Support vector machines.

COMPUTERS / Computer Vision & Pattern Recognition.

Class number

System Code

Kung, S. Y., (Sun Yuan)

Date of Transaction

20140715043100.0

Cataloguing Rules (Descriptive Conventions))

rda

Electronic name

[Book]

Y