Gaussian processes for machine learning / (Record no. 68759)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02109nam a22001817a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 211013b2006 |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780262182539 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 519.21 RAS-C |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Rasmussen, Carl Edward |
| 245 ## - TITLE STATEMENT | |
| Title | Gaussian processes for machine learning / |
| Statement of responsibility, etc. | Carl Edward Rasmussen and Christopher K. I. Williams |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | London |
| Name of publisher, distributor, etc. | The MIT Press |
| Date of publication, distribution, etc. | 2006 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 248 p. |
| 365 ## - TRADE PRICE | |
| Price type code | USD |
| Price amount | 50.00. |
| 500 ## - GENERAL NOTE | |
| General note | A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.<br/>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. The book deals with the supervised-learning problem for both regression and classification and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning--Mathematical models |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Gaussian processes--Data processing |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Williams, Christopher K. I. |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) | |
| Withdrawn status | |
| Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Current library | Shelving location | Date acquired | Total Checkouts | Full call number | Barcode | Date last seen | Date last checked out | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | 510 | BITS Pilani Hyderabad | General Stack (For lending) | 13/10/2021 | 1 | 519.21 RAS-C | 42431 | 13/07/2024 | 05/04/2022 | Books |