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...
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| Tác giả chính: | |
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| Tác giả khác: | |
| Ngôn ngữ: | Undetermined English |
| Được phát hành: |
New York
Springer
2006
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| Những chủ đề: | |
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| Thư viện lưu trữ: | Trung tâm Học liệu Trường Đại học Trà Vinh |
|---|
| LEADER | 01366nam a2200265Ia 4500 | ||
|---|---|---|---|
| 001 | TVU_18562 | ||
| 008 | 210423s9999 xx 000 0 und d | ||
| 020 | |a 0387310738 | ||
| 020 | |a 9780387310732 | ||
| 041 | |a eng | ||
| 082 | |a 6.4 | ||
| 082 | |b B313 | ||
| 100 | |a Bishop, Christopher M. | ||
| 245 | 0 | |a Pattern recognition and machine learning | |
| 245 | 0 | |c Christopher M. Bishop | |
| 260 | |a New York | ||
| 260 | |b Springer | ||
| 260 | |c 2006 | ||
| 300 | |a xx, 738 p. | ||
| 300 | |b ill. (some col.) | ||
| 300 | |c 25 cm | ||
| 520 | |a 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 | ||
| 650 | |a Pattern perception; Machine learning | ||
| 700 | |a Christopher M. Bishop | ||
| 980 | |a Trung tâm Học liệu Trường Đại học Trà Vinh | ||