Gespeichert in:
| Beteilige Person: | |
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| Format: | E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2000
|
| Links: | https://doi.org/10.1017/CBO9780511801389 |
| Zusammenfassung: | This is the first comprehensive introduction to Support Vector Machines (SVMs), a generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software. |
| Umfang: | 1 Online-Ressource (xiii, 189 Seiten) |
| ISBN: | 9780511801389 |
Internformat
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| 100 | 1 | |a Cristianini, Nello | |
| 245 | 1 | 3 | |a An introduction to support vector machines |b and other kernel-based learning methods |c Nello Cristianini and John Shawe-Taylor |
| 246 | 3 | |a An Introduction to Support Vector Machines & Other Kernel-based Learning Methods | |
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| spelling | Cristianini, Nello An introduction to support vector machines and other kernel-based learning methods Nello Cristianini and John Shawe-Taylor An Introduction to Support Vector Machines & Other Kernel-based Learning Methods Cambridge Cambridge University Press 2000 1 Online-Ressource (xiii, 189 Seiten) txt c cr This is the first comprehensive introduction to Support Vector Machines (SVMs), a generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software. Shawe-Taylor, John Erscheint auch als Druck-Ausgabe 9780521780193 |
| spellingShingle | Cristianini, Nello An introduction to support vector machines and other kernel-based learning methods |
| title | An introduction to support vector machines and other kernel-based learning methods |
| title_alt | An Introduction to Support Vector Machines & Other Kernel-based Learning Methods |
| title_auth | An introduction to support vector machines and other kernel-based learning methods |
| title_exact_search | An introduction to support vector machines and other kernel-based learning methods |
| title_full | An introduction to support vector machines and other kernel-based learning methods Nello Cristianini and John Shawe-Taylor |
| title_fullStr | An introduction to support vector machines and other kernel-based learning methods Nello Cristianini and John Shawe-Taylor |
| title_full_unstemmed | An introduction to support vector machines and other kernel-based learning methods Nello Cristianini and John Shawe-Taylor |
| title_short | An introduction to support vector machines |
| title_sort | introduction to support vector machines and other kernel based learning methods |
| title_sub | and other kernel-based learning methods |
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