{"product_id":"9781139861892","title":"Kernel Methods and Machine Learning","description":"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.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":47107914105072,"sku":"9781139861892","price":80.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/7593\/9824\/files\/9781139861892_p0.jpg?v=1763701238","url":"https:\/\/shop-qa.barnesandnoble.com\/products\/9781139861892","provider":"Barnes \u0026 Noble (DEV)","version":"1.0","type":"link"}