Kernel methods and machine learning Provides an overview of the broad spectrum of applications and problem formulations for kernel based unsupervised and supervised learning methods. The dimension of the original vector space along with its Euclidean inner product often proves to be highly inadequate for complex data analysis. In order to provide a more e 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