Kernel Methods and Machine Learning

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.