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Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning: 17 (Studies in Computational Intelligence) - Tapa dura
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This 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 using support vector machines (SVMs) in an iterative way. It demonstrates how kernel based SVMs can be used for dimensionality reduction and shows the similarities and differences between the two most popular unsupervised techniques.
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"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.
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- EditorialSpringer
- Año de publicación2006
- ISBN 10 3540316817
- ISBN 13 9783540316817
- EncuadernaciónTapa dura
- IdiomaInglés
- Número de páginas284
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Kernel Based Algorithms For Mining Huge Data Sets
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Kernel Based Algorithms For Mining Huge Data Sets
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Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning (Studies in Computational Intelligence, 17)
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Kernel Based Algorithms for Mining Huge Data Sets
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Springer Berlin Heidelberg Mrz 2006, 2006
ISBN 10: 3540316817
ISBN 13: 9783540316817
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This 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 using support vector machines (SVMs) in an iterative way. It demonstrates how kernel based SVMs can be used for dimensionality reduction and shows the similarities and differences between the two most popular unsupervised techniques. 284 pp. Englisch. Nº de ref. del artículo: 9783540316817
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Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning (Studies in Computational Intelligence, 17)
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Kernel Based Algorithms for Mining Huge Data Sets : Supervised, Semi-supervised, and Unsupervised Learning
Publicado por
Springer Berlin Heidelberg, 2006
ISBN 10: 3540316817
ISBN 13: 9783540316817
Nuevo
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - '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. Nº de ref. del artículo: 9783540316817
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Kernel Based Algorithms for Mining Huge Data Sets
Publicado por
Springer Berlin Heidelberg, 2006
ISBN 10: 3540316817
ISBN 13: 9783540316817
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Librería: moluna, Greven, Alemania
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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Reports recent research results on Kernel Based Algorithms for Mining Huge Data SetsA book about (machine) learning from (experimental) data This is the first book treating the fields of supervised, semi-supervised an. Nº de ref. del artículo: 4887602
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Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning (Studies in Computational Intelligence (17))
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Hardcover. Condición: Like New. Like New. book. Nº de ref. del artículo: ERICA75835403168175
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