Discriminative and Generative
Edición de la obra Machine Learning
| Autor | Tony Jebara |
|---|---|
| Editorial | Springer US |
| Fecha de publicación | 2004 |
| Lugar | Boston, MA |
| Idioma | inglés |
| Páginas | 200 |
| Formato | [electronic resource] : |
| ISBN-13 | 9781461347569, 9781441990112 |
| ISBN-10 | 1461347564, 1441990119 |
| OCLC | 853259270 |
| Serie | The International Series in Engineering and Computer Science -- 755 · International series in engineering and computer science -- 755. |
| Número de Cutter | J44m |
|---|
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.