Aleksandr Aravkin; Anna Choromanska; Li Deng; Georg Heigold; Tony Jebara; Dimitri Kanevsky; Stephen J. Wright MIT Press Ltd (2018) Saatavuus: Painos loppu Kovakantinen kirja
Springer Sivumäärä: 200 sivua Asu: Kovakantinen kirja Painos: 2004 Julkaisuvuosi: 2003, 31.12.2003 (lisätietoa) Kieli: Englanti
Machine Learning:Discriminative and Generative covers the main contemporary themes and tools in machine learning ranging from Bayesian probabilistic models to discriminative support-vector machines. However, unlike previous books that only discuss these rather different approaches in isolation, it bridges the two schools of thought together within a common framework, elegantly connecting their various theories and making one common big-picture. Also, this bridge brings forth new hybrid discriminative-generative tools that combine the strengths of both camps. This book serves multiple purposes as well. The framework acts as a scientific breakthrough, fusing the areas of generative and discriminative learning and will be of interest to many researchers. However, as a conceptual breakthrough, this common framework unifies many previously unrelated tools and techniques and makes them understandable to a larger portion of the public. This gives the more practical-minded engineer, student and the industrial public an easy-access and more sensible road map into the world of machine learning.
Machine Learning: Discriminative and Generative is designed for an audience composed of researchers & practitioners in industry and academia. The book is also suitable as a secondary text for graduate-level students in computer science and engineering.