Pattern recognition and machine learning
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no othe...
Gardado en:
| Autor Principal: | |
|---|---|
| Outros autores: | |
| Idioma: | Undetermined English |
| Publicado: |
New York
Springer
2006
|
| Những chủ đề: | |
| Các nhãn: |
Engadir etiqueta
Sen Etiquetas, Sexa o primeiro en etiquetar este rexistro!
|
| Thư viện lưu trữ: | Trung tâm Học liệu Trường Đại học Trà Vinh |
|---|
| Tóm tắt: | This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory |
|---|---|
| Descrición Física: | xx, 738 p. ill. (some col.) 25 cm |
| ISBN: | 0387310738 9780387310732 |