Gaussian processes for machine learning / / Carl Edward Rasmussen, Christopher K.I. Williams.
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.
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| Superior document: | Adaptive computation and machine learning |
|---|---|
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| Year of Publication: | 2006 |
| Edition: | 1st ed. |
| Language: | English |
| Series: | Adaptive computation and machine learning.
|
| Physical Description: | xviii, 248 p. :; ill. |
| Notes: | Title from title screen. |
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Rasmussen, Carl Edward. Gaussian processes for machine learning / Carl Edward Rasmussen, Christopher K.I. Williams. 1st ed. Computer document. Cambridge, Mass. : MIT Press, cop. 2006. xviii, 248 p. : ill. text txt rdacontent computer c rdamedia online resource cr rdacarrier Adaptive computation and machine learning Title from title screen. Text. Digitized and made available by: Books24x7.com. Available also in a print ed. Includes bibliographical references (p. [223]-238) and indexes. A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Intro -- Series Foreword -- Preface -- Symbols and Notation -- Chapter 1 Introduction -- Chapter 2 Regression -- Chapter 3 Classification -- Chapter 4 Covariance functions -- Chapter 5 Model Selection and Adaptation of Hyperparameters -- Chapter 6 Relationships between GPs and Other Models -- Chapter 7 Theoretical Perspectives -- Chapter 8 Approximation Methods for Large Datasets -- Chapter 9 Further Issues and Conclusions -- Appendix A Mathematical Background -- Appendix B Gaussian Markov Processes -- Appendix C Datasets and Code -- Bibliography -- Author Index -- Subject Index. Aprenentatge automàtic Models matemàtics lemac Processos gaussians Informàtica lemac Gaussian processes Data processing. Machine learning Mathematical models. Williams, Christopher K. I. 0-262-18253-X Adaptive computation and machine learning. |
| language |
English |
| format |
eBook |
| author |
Rasmussen, Carl Edward. |
| spellingShingle |
Rasmussen, Carl Edward. Gaussian processes for machine learning / Adaptive computation and machine learning Intro -- Series Foreword -- Preface -- Symbols and Notation -- Chapter 1 Introduction -- Chapter 2 Regression -- Chapter 3 Classification -- Chapter 4 Covariance functions -- Chapter 5 Model Selection and Adaptation of Hyperparameters -- Chapter 6 Relationships between GPs and Other Models -- Chapter 7 Theoretical Perspectives -- Chapter 8 Approximation Methods for Large Datasets -- Chapter 9 Further Issues and Conclusions -- Appendix A Mathematical Background -- Appendix B Gaussian Markov Processes -- Appendix C Datasets and Code -- Bibliography -- Author Index -- Subject Index. |
| author_facet |
Rasmussen, Carl Edward. Williams, Christopher K. I. |
| author_variant |
c e r ce cer |
| author2 |
Williams, Christopher K. I. |
| author2_variant |
c k i w cki ckiw |
| author2_role |
TeilnehmendeR |
| author_sort |
Rasmussen, Carl Edward. |
| title |
Gaussian processes for machine learning / |
| title_full |
Gaussian processes for machine learning / Carl Edward Rasmussen, Christopher K.I. Williams. |
| title_fullStr |
Gaussian processes for machine learning / Carl Edward Rasmussen, Christopher K.I. Williams. |
| title_full_unstemmed |
Gaussian processes for machine learning / Carl Edward Rasmussen, Christopher K.I. Williams. |
| title_auth |
Gaussian processes for machine learning / |
| title_new |
Gaussian processes for machine learning / |
| title_sort |
gaussian processes for machine learning / |
| series |
Adaptive computation and machine learning |
| series2 |
Adaptive computation and machine learning |
| publisher |
MIT Press, |
| publishDate |
2006 |
| physical |
xviii, 248 p. : ill. Available also in a print ed. |
| edition |
1st ed. |
| contents |
Intro -- Series Foreword -- Preface -- Symbols and Notation -- Chapter 1 Introduction -- Chapter 2 Regression -- Chapter 3 Classification -- Chapter 4 Covariance functions -- Chapter 5 Model Selection and Adaptation of Hyperparameters -- Chapter 6 Relationships between GPs and Other Models -- Chapter 7 Theoretical Perspectives -- Chapter 8 Approximation Methods for Large Datasets -- Chapter 9 Further Issues and Conclusions -- Appendix A Mathematical Background -- Appendix B Gaussian Markov Processes -- Appendix C Datasets and Code -- Bibliography -- Author Index -- Subject Index. |
| isbn |
0-262-26107-3 9786612097966 1-4237-6990-2 0-262-18253-X |
| callnumber-first |
Q - Science |
| callnumber-subject |
QA - Mathematics |
| callnumber-label |
QA274 |
| callnumber-sort |
QA 3274.4 R37 42006 |
| illustrated |
Illustrated |
| dewey-hundreds |
500 - Science |
| dewey-tens |
510 - Mathematics |
| dewey-ones |
519 - Probabilities & applied mathematics |
| dewey-full |
519.2/3 |
| dewey-sort |
3519.2 13 |
| dewey-raw |
519.2/3 |
| dewey-search |
519.2/3 |
| oclc_num |
68194203 |
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AT rasmussencarledward gaussianprocessesformachinelearning AT williamschristopherki gaussianprocessesformachinelearning |
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Gaussian processes for machine learning / |
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