Least squares support vector machines
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 expla...
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| Format: | Elektronisch E-Book |
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| Sprache: | English |
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World Scientific Pub. Co.
c2002
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| Online-Zugang: | DE-92 URL des Erstveroeffentlichers |
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| 245 | 1 | 0 | |a Least squares support vector machines |c Johan A. K. Suykens ... [et al.] |
| 264 | 1 | |a Singapore |b World Scientific Pub. Co. |c c2002 | |
| 300 | |a xiv, 294 p. |b ill | ||
| 336 | |b txt |2 rdacontent | ||
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| 520 | |a 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 sparseness 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 Nyström sampling with active selection of support vectors. The methods are illustrated with several examples | ||
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Algorithms | |
| 650 | 4 | |a Kernel functions | |
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| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Lernendes System |0 (DE-588)4120666-6 |2 gnd |9 rswk-swf |
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| 700 | 1 | |a Suykens, Johan A. K. |e Sonstige |4 oth | |
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Datensatz im Suchindex
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| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
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| dewey-search | 006.31 |
| dewey-sort | 16.31 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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| id | DE-604.BV044635087 |
| illustrated | Illustrated |
| indexdate | 2024-12-24T06:13:55Z |
| institution | BVB |
| isbn | 9789812776655 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030033059 |
| oclc_num | 881299053 |
| open_access_boolean | |
| owner | DE-92 |
| owner_facet | DE-92 |
| physical | xiv, 294 p. ill |
| psigel | ZDB-124-WOP ZDB-124-WOP FHN_PDA_WOP |
| publishDate | 2002 |
| publishDateSearch | 2002 |
| publishDateSort | 2002 |
| publisher | World Scientific Pub. Co. |
| record_format | marc |
| spelling | Least squares support vector machines Johan A. K. Suykens ... [et al.] Singapore World Scientific Pub. Co. c2002 xiv, 294 p. ill txt rdacontent c rdamedia cr rdacarrier 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 sparseness 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 Nyström sampling with active selection of support vectors. The methods are illustrated with several examples Machine learning Algorithms Kernel functions Least squares Support-Vektor-Maschine (DE-588)4505517-8 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Lernendes System (DE-588)4120666-6 gnd rswk-swf Support-Vektor-Maschine (DE-588)4505517-8 s Maschinelles Lernen (DE-588)4193754-5 s Lernendes System (DE-588)4120666-6 s 1\p DE-604 Suykens, Johan A. K. Sonstige oth Erscheint auch als Druck-Ausgabe 9789812381514 Erscheint auch als Druck-Ausgabe 9812381511 (alk. paper) http://www.worldscientific.com/worldscibooks/10.1142/5089#t=toc Verlag URL des Erstveroeffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Least squares support vector machines Machine learning Algorithms Kernel functions Least squares Support-Vektor-Maschine (DE-588)4505517-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Lernendes System (DE-588)4120666-6 gnd |
| subject_GND | (DE-588)4505517-8 (DE-588)4193754-5 (DE-588)4120666-6 |
| title | Least squares support vector machines |
| 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 ... [et al.] |
| title_fullStr | Least squares support vector machines Johan A. K. Suykens ... [et al.] |
| title_full_unstemmed | Least squares support vector machines Johan A. K. Suykens ... [et al.] |
| title_short | Least squares support vector machines |
| title_sort | least squares support vector machines |
| topic | Machine learning Algorithms Kernel functions Least squares Support-Vektor-Maschine (DE-588)4505517-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Lernendes System (DE-588)4120666-6 gnd |
| topic_facet | Machine learning Algorithms Kernel functions Least squares Support-Vektor-Maschine Maschinelles Lernen Lernendes System |
| url | http://www.worldscientific.com/worldscibooks/10.1142/5089#t=toc |
| work_keys_str_mv | AT suykensjohanak leastsquaressupportvectormachines |