Kernel Methods and Machine Learning

Discover the essential resource for mastering kernel-based learning theory with Kernel Methods and Machine Learning by S. Y. Kung, published by Cambridge University Press in 2014. This comprehensive hardback edition spans 572 pages and is designed for graduate students and professionals in fields such as computer science, electrical engineering, and biomedical engineering.

Delve into numerous algorithms and major theorems presented in a clear, step-by-step format. With over two hundred problems and real-world examples, this book not only enhances your understanding but also provides practical applications of kernel methods. Instructors will appreciate the online solutions available for all problems, making it an ideal choice for classroom settings. Elevate your expertise in machine learning with this invaluable guide!