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Ajouter au panierEtat : New. pp. 278.
Langue: anglais
Edité par Springer Berlin Heidelberg, 2006
ISBN 10 : 3642068561 ISBN 13 : 9783642068560
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Ajouter au panierPaperback. Etat : Brand New. 260 pages. 9.00x6.00x0.63 inches. In Stock.
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Ajouter au panierTaschenbuch. Etat : Neu. Kernel Based Algorithms for Mining Huge Data Sets | Supervised, Semi-supervised, and Unsupervised Learning | Te-Ming Huang (u. a.) | Taschenbuch | Studies in Computational Intelligence | xvi | Englisch | 2010 | Springer | EAN 9783642068560 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Ajouter au panierBuch. Etat : 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.
Langue: anglais
Edité par Springer Berlin Heidelberg, 2010
ISBN 10 : 3642068561 ISBN 13 : 9783642068560
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Ajouter au panierTaschenbuch. Etat : 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.
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Ajouter au panierPaperback. Etat : Like New. Like New. book.
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Ajouter au panierEtat : new. Questo è un articolo print on demand.
Langue: anglais
Edité par Springer Berlin Heidelberg Mrz 2006, 2006
ISBN 10 : 3540316817 ISBN 13 : 9783540316817
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Ajouter au panierBuch. Etat : 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.
Langue: anglais
Edité par Springer Berlin Heidelberg Nov 2010, 2010
ISBN 10 : 3642068561 ISBN 13 : 9783642068560
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
EUR 106,99
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Ajouter au panierTaschenbuch. Etat : 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. 276 pp. Englisch.
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Ajouter au panierEtat : New. Print on Demand pp. 278 96 Illus.
Langue: anglais
Edité par Springer Berlin Heidelberg, 2010
ISBN 10 : 3642068561 ISBN 13 : 9783642068560
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Ajouter au panierEtat : 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.
Langue: anglais
Edité par Springer Berlin Heidelberg, 2006
ISBN 10 : 3540316817 ISBN 13 : 9783540316817
Vendeur : moluna, Greven, Allemagne
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Ajouter au panierGebunden. Etat : 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.
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Ajouter au panierEtat : New. PRINT ON DEMAND pp. 278.
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Ajouter au panierBuch. Etat : Neu. Kernel Based Algorithms for Mining Huge Data Sets | Supervised, Semi-supervised, and Unsupervised Learning | Te-Ming Huang (u. a.) | Buch | Studies in Computational Intelligence | xvi | Englisch | 2006 | Springer | EAN 9783540316817 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.
Langue: anglais
Edité par Springer, Springer Mär 2006, 2006
ISBN 10 : 3540316817 ISBN 13 : 9783540316817
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
EUR 106,99
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Ajouter au panierBuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -'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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 284 pp. Englisch.
Langue: anglais
Edité par Springer, Springer Nov 2010, 2010
ISBN 10 : 3642068561 ISBN 13 : 9783642068560
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
EUR 106,99
Quantité disponible : 1 disponible(s)
Ajouter au panierTaschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -'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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 276 pp. Englisch.