Least squares support vector machines /:
Annotation.
Gespeichert in:
| Weitere Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
River Edge, NJ :
World Scientific,
2002.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Annotation. |
| Beschreibung: | 1 online resource (xiv, 294 pages) : illustrations |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9812776656 9789812776655 |
Internformat
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| 245 | 0 | 0 | |a Least squares support vector machines / |c Johan A.K. Suykens [and others]. |
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| 504 | |a Includes bibliographical references and index. | ||
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| 520 | 8 | |a Annotation. |b This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing spareness and employing robust statistics. The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nystrom sampling with active selection of support vectors. The methods are illustrated with several examples. | |
| 505 | 0 | 0 | |g Ch. 1. |t Introduction -- |g Ch. 2. |t Support Vector Machines -- |g Ch. 3. |t Basic Methods of Least Squares Support Vector Machines -- |g Ch. 4. |t Bayesian Inference for LS-SVM Models -- |g Ch. 5. |t Robustness -- |g Ch. 6. |t Large Scale Problems -- |g Ch. 7. |t LS-SVM for Unsupervised Learning -- |g Ch. 8. |t LS-SVM for Recurrent Networks and Control. |
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| 700 | 1 | |a Suykens, Johan A. K. |0 http://id.loc.gov/authorities/names/n95114980 | |
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Datensatz im Suchindex
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| _version_ | 1816881686337552384 |
| adam_text | |
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| author2 | Suykens, Johan A. K. |
| author2_role | |
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| author_GND | http://id.loc.gov/authorities/names/n95114980 |
| author_facet | Suykens, Johan A. K. |
| author_sort | Suykens, Johan A. K. |
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| contents | Introduction -- Support Vector Machines -- Basic Methods of Least Squares Support Vector Machines -- Bayesian Inference for LS-SVM Models -- Robustness -- Large Scale Problems -- LS-SVM for Unsupervised Learning -- LS-SVM for Recurrent Networks and Control. |
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| illustrated | Illustrated |
| indexdate | 2024-11-27T13:16:40Z |
| institution | BVB |
| isbn | 9812776656 9789812776655 |
| language | English |
| oclc_num | 305127050 |
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| spelling | Least squares support vector machines / Johan A.K. Suykens [and others]. River Edge, NJ : World Scientific, 2002. 1 online resource (xiv, 294 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references and index. Print version record. Annotation. This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing spareness and employing robust statistics. The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nystrom sampling with active selection of support vectors. The methods are illustrated with several examples. Ch. 1. Introduction -- Ch. 2. Support Vector Machines -- Ch. 3. Basic Methods of Least Squares Support Vector Machines -- Ch. 4. Bayesian Inference for LS-SVM Models -- Ch. 5. Robustness -- Ch. 6. Large Scale Problems -- Ch. 7. LS-SVM for Unsupervised Learning -- Ch. 8. LS-SVM for Recurrent Networks and Control. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Algorithms. http://id.loc.gov/authorities/subjects/sh85003487 Kernel functions. http://id.loc.gov/authorities/subjects/sh85072061 Least squares. http://id.loc.gov/authorities/subjects/sh85075570 Algorithms https://id.nlm.nih.gov/mesh/D000465 Least-Squares Analysis https://id.nlm.nih.gov/mesh/D016018 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Algorithmes. Noyaux (Mathématiques) Moindres carrés. algorithms. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Algorithms fast Kernel functions fast Least squares fast Machine learning fast Suykens, Johan A. K. http://id.loc.gov/authorities/names/n95114980 Print version: Least squares support vector machines. River Edge, NJ : World Scientific, 2002 (DLC) 2002033063 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=210682 Volltext |
| spellingShingle | Least squares support vector machines / Introduction -- Support Vector Machines -- Basic Methods of Least Squares Support Vector Machines -- Bayesian Inference for LS-SVM Models -- Robustness -- Large Scale Problems -- LS-SVM for Unsupervised Learning -- LS-SVM for Recurrent Networks and Control. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Algorithms. http://id.loc.gov/authorities/subjects/sh85003487 Kernel functions. http://id.loc.gov/authorities/subjects/sh85072061 Least squares. http://id.loc.gov/authorities/subjects/sh85075570 Algorithms https://id.nlm.nih.gov/mesh/D000465 Least-Squares Analysis https://id.nlm.nih.gov/mesh/D016018 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Algorithmes. Noyaux (Mathématiques) Moindres carrés. algorithms. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Algorithms fast Kernel functions fast Least squares fast Machine learning fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh85003487 http://id.loc.gov/authorities/subjects/sh85072061 http://id.loc.gov/authorities/subjects/sh85075570 https://id.nlm.nih.gov/mesh/D000465 https://id.nlm.nih.gov/mesh/D016018 https://id.nlm.nih.gov/mesh/D000069550 |
| title | Least squares support vector machines / |
| title_alt | Introduction -- Support Vector Machines -- Basic Methods of Least Squares Support Vector Machines -- Bayesian Inference for LS-SVM Models -- Robustness -- Large Scale Problems -- LS-SVM for Unsupervised Learning -- LS-SVM for Recurrent Networks and Control. |
| title_auth | Least squares support vector machines / |
| title_exact_search | Least squares support vector machines / |
| title_full | Least squares support vector machines / Johan A.K. Suykens [and others]. |
| title_fullStr | Least squares support vector machines / Johan A.K. Suykens [and others]. |
| title_full_unstemmed | Least squares support vector machines / Johan A.K. Suykens [and others]. |
| title_short | Least squares support vector machines / |
| title_sort | least squares support vector machines |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Algorithms. http://id.loc.gov/authorities/subjects/sh85003487 Kernel functions. http://id.loc.gov/authorities/subjects/sh85072061 Least squares. http://id.loc.gov/authorities/subjects/sh85075570 Algorithms https://id.nlm.nih.gov/mesh/D000465 Least-Squares Analysis https://id.nlm.nih.gov/mesh/D016018 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Algorithmes. Noyaux (Mathématiques) Moindres carrés. algorithms. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Algorithms fast Kernel functions fast Least squares fast Machine learning fast |
| topic_facet | Machine learning. Algorithms. Kernel functions. Least squares. Algorithms Least-Squares Analysis Machine Learning Apprentissage automatique. Algorithmes. Noyaux (Mathématiques) Moindres carrés. algorithms. COMPUTERS Enterprise Applications Business Intelligence Tools. COMPUTERS Intelligence (AI) & Semantics. Kernel functions Least squares Machine learning |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=210682 |
| work_keys_str_mv | AT suykensjohanak leastsquaressupportvectormachines |