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Understanding
9/11Pattern recognition and machine learning
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xx, 738 p. : 25 cm
Textbook for graduates
Includes bibliographical references (p. 711-728) and index
1. Introduction -- 2. Probability distributions -- 3. Linear models for regression -- 4. Linear models for classification -- 5. Neural networks -- 6. Kernel methods -- 7. Sparse kernel machines -- 8. Graphical models -- 9. Mixture models and EM -- 10. Approximate inference -- 11. Sampling methods -- 12. Continuous latent variables -- 13. Sequential data -- 14. Combining models
Textbook for graduates
Includes bibliographical references (p. 711-728) and index
1. Introduction -- 2. Probability distributions -- 3. Linear models for regression -- 4. Linear models for classification -- 5. Neural networks -- 6. Kernel methods -- 7. Sparse kernel machines -- 8. Graphical models -- 9. Mixture models and EM -- 10. Approximate inference -- 11. Sampling methods -- 12. Continuous latent variables -- 13. Sequential data -- 14. Combining models
- Access-restricted-item
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- Addeddate
- 2023-05-13 09:11:50
- Autocrop_version
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- Bookplateleaf
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- Boxid
- IA40930602
- Camera
- Sony Alpha-A6300 (Control)
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urn:lcp:patternrecogniti0000bish:epub:9ca6a8f9-f0da-4c36-99d1-3f07074336b9
urn:lcp:patternrecogniti0000bish:lcpdf:fb5445ae-97aa-4dee-93d9-a9de58e6c31a
urn:oclc:record:1392330582![[WorldCat (this item)]](https://archive.org/images/worldcat-small.png "[WorldCat (this item)]")
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- Invoice
- 1652
