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_aDLC _beng _erda _dATU |
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| 042 | _apcc | ||
| 050 | 0 | 0 |
_aQA274.4 _b.R37 2006 |
| 082 | 0 | 0 |
_a519.23 _222 |
| 100 | 1 |
_aRasmussen, Carl Edward, _eauthor. _91062267 |
|
| 245 | 1 | 0 |
_aGaussian processes for machine learning / _cCarl Edward Rasmussen, Christopher K.I. Williams. |
| 264 | 1 |
_aCambridge, Mass. : _bMIT Press, _c[2006] |
|
| 264 | 4 | _c©2006 | |
| 300 |
_axviii, 248 pages : _billustrations ; _c26 cm. |
||
| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_aunmediated _bn _2rdamedia |
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| 338 |
_avolume _bnc _2rdacarrier |
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| 490 | 1 | _aAdaptive computation and machine learning | |
| 504 | _aIncludes bibliographical references (pages 223-238) and index. | ||
| 505 | 0 | 0 |
_g1. _tIntroduction -- _g2. _tRegression -- _g3. _tClassification -- _g4. _tCovariance functions -- _g5. _tModel selection and adaptation of hyperparameters -- _g6. _tRelationships between GPs and other models -- _g7. _tTheoretical perspectives -- _g8. _tApproximation methods for large datasets -- _g9. _tFurther issues and conclusions. |
| 520 | 1 | _a"Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics."--BOOK JACKET. | |
| 588 | _aMachine converted from AACR2 source record. | ||
| 650 | 0 |
_aGaussian processes _xData processing _9779461 |
|
| 650 | 0 |
_aMachine learning _xMathematical models _9718591 |
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| 700 | 1 |
_aWilliams, Christopher K. I., _eauthor. _9248887 |
|
| 830 | 0 |
_aAdaptive computation and machine learning. _91021113 |
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| 907 |
_a.b11219968 _b03-10-17 _c27-10-15 |
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