Kernel based algorithms for mining huge data sets : supervised, semi-supervised, and unsupervised learning
"Kernel Based Algorithms for Mining Huge Data Sets" is the first book treating the fields of supervised, semi-supervised and unsupervised machine learning collectively. The book presents both the theory and the algorithms for mining huge data sets by using support vector machines (SVMs) in...
Shranjeno v:
| Auteurs principaux: | , , |
|---|---|
| Format: | Livre numérique |
| Jezik: | Anglais |
| Izdano: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
2006.
Cham : Springer Nature |
| Serija: | Studies in Computational Intelligence
17 |
| Teme: | |
| Online dostop: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Sporočilo: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Kernel Based Algorithms for Mining Huge Data Sets, Texte imprimé, 9783642068560 • Kernel Based Algorithms for Mining Huge Data Sets, Texte imprimé, 9783540819974 • Kernel Based Algorithms for Mining Huge Data Sets, Texte imprimé, 9783540316817 |
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| 020 | |a 9783540316893 (en ligne) | ||
| 020 | |a 9783540316893 | ||
| 041 | 0 | |a eng | |
| 082 | |a 519.3 | ||
| 100 | 1 | |a Huang, Te-Ming. | |
| 245 | 1 | 0 | |a Kernel based algorithms for mining huge data sets : |b supervised, semi-supervised, and unsupervised learning |c Te-Ming Huang, Vojislav Kecman, Ivica Kopriva. |
| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg. | ||
| 260 | |a Cham : |b Springer Nature, |c 2006. | ||
| 490 | 0 | |a Studies in Computational Intelligence |v 17 |x 1860-9503 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 0 | |a Support Vector Machines in Classification and Regression An Introduction -- Iterative Single Data Algorithm for Kernel Machines from Huge Data Sets: Theory and Performance -- Feature Reduction with Support Vector Machines and Application in DNA Microarray Analysis -- Semi-supervised Learning and Applications -- Unsupervised Learning by Principal and Independent Component Analysis. | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. chttps://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a "Kernel Based Algorithms for Mining Huge Data Sets" is the first book treating the fields of supervised, semi-supervised and unsupervised machine learning collectively. The book presents both the theory and the algorithms for mining huge data sets by using support vector machines (SVMs) in an iterative way. It demonstrates how kernel based SVMs can be used for dimensionality reduction (feature elimination) and shows the similarities and differences between the two most popular unsupervised techniques, the principal component analysis (PCA) and the independent component analysis (ICA). The book presents various examples, software, algorithmic solutions enabling the reader to develop their own codes for solving the problems. The book is accompanied by a website for downloading both data and software for huge data sets modeling in a supervised and semisupervised manner, as well as MATLAB based PCA and ICA routines for unsupervised learning. The book focuses on a broad range of machine learning algorithms and it is particularly aimed at students, scientists, and practicing researchers in bioinformatics (gene microarrays), text-categorization, numerals recognition, as well as in the images and audio signals de-mixing (blind source separation) areas. | ||
| 533 | |n Reproduction électronique. Boulder, Colo. : NetLibrary, 2006 | ||
| 650 | |a Reconnaissance des formes (informatique) | ||
| 700 | 1 | |a Kecman, Vojislav, |d 1948- |4 aut | |
| 700 | 1 | |a Kopriva, Ivica. |4 aut | |
| 776 | 0 | |t Kernel Based Algorithms for Mining Huge Data Sets |b Texte imprimé |z 9783642068560 | |
| 776 | 0 | |t Kernel Based Algorithms for Mining Huge Data Sets |b Texte imprimé |z 9783540819974 | |
| 776 | 0 | |t Kernel Based Algorithms for Mining Huge Data Sets |b Texte imprimé |z 9783540316817 | |
| 787 | 0 | 8 | |i Reproduction of: |0 102556989 |t Kernel based algorithms for mining huge data sets |o supervised, semi-supervised, and unsupervised learning |f Te-Ming Huang, Vojislav Kecman, Ivica Kopriva |c Berlin |n Springer |d 2006 |p 1 vol. (xvi, 260 p.) |s Studies in computational intelligence |z 3-540-31681-7 |
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