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Kernel Based Algorithms Mining by Huang Te Ming (24 results)
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£ 84.34
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Add to basketCondition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
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Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning (Studies in Computational Intelligence, 17)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Kernel Based Algorithms for Mining Huge Data Sets : Supervised, Semi-supervised, And Unsupervised Learning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Kernel Based Algorithms for Mining Huge Data Sets : Supervised, Semi-supervised, and Unsupervised Learning
Published by Springer Berlin Heidelberg, 2010
ISBN 10: 3642068561 ISBN 13: 9783642068560
Language: English
£ 94.94
Convert currency£ 12.05 shipping from Germany to United KingdomQuantity: 1 available
Add to basketTaschenbuch. Condition: 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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Kernel Based Algorithms for Mining Huge Data Sets : Supervised, Semi-supervised, and Unsupervised Learning
Published by Springer Berlin Heidelberg, 2006
ISBN 10: 3540316817 ISBN 13: 9783540316817
Language: English
£ 94.94
Convert currency£ 12.05 shipping from Germany to United KingdomQuantity: 1 available
Add to basketBuch. Condition: 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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Kernel Based Algorithms for Mining Huge Data Sets
Published by Springer Berlin Heidelberg, Springer Berlin Heidelberg Mär 2006, 2006
ISBN 10: 3540316817 ISBN 13: 9783540316817
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
£ 94.94
Convert currency£ 30.15 shipping from Germany to United KingdomQuantity: 2 available
Add to basketBuch. Condition: Neu. 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 GmbH, Tiergartenstr. 17, 69121 Heidelberg 284 pp. Englisch.
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Add to basketCondition: New. pp. 278.
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Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning
Published by Springer Berlin Heidelberg, 2006
ISBN 10: 3642068561 ISBN 13: 9783642068560
Language: English
Paperback. Condition: Brand New. 260 pages. 9.00x6.00x0.63 inches. In Stock.
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Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning (Studies in Computational Intelligence, 17)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Kernel Based Algorithms for Mining Huge Data Sets : Supervised, Semi-supervised, And Unsupervised Learning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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£ 182.19
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Kernel Based Algorithms for Mining Huge Data Sets
Published by Springer Berlin Heidelberg, 2010
ISBN 10: 3642068561 ISBN 13: 9783642068560
Language: English
£ 81.88
Convert currency£ 21.53 shipping from Germany to United KingdomQuantity: Over 20 available
Add to basketCondition: 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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Kernel Based Algorithms for Mining Huge Data Sets
Published by Springer Berlin Heidelberg, 2006
ISBN 10: 3540316817 ISBN 13: 9783540316817
Language: English
£ 81.88
Convert currency£ 21.53 shipping from Germany to United KingdomQuantity: Over 20 available
Add to basketGebunden. Condition: 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.
-
Kernel Based Algorithms for Mining Huge Data Sets
Published by Springer Berlin Heidelberg Nov 2010, 2010
ISBN 10: 3642068561 ISBN 13: 9783642068560
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
£ 94.94
Convert currency£ 9.48 shipping from Germany to United KingdomQuantity: 2 available
Add to basketTaschenbuch. Condition: 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.
-
Kernel Based Algorithms for Mining Huge Data Sets
Published by Springer Berlin Heidelberg Mrz 2006, 2006
ISBN 10: 3540316817 ISBN 13: 9783540316817
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
£ 94.94
Convert currency£ 9.48 shipping from Germany to United KingdomQuantity: 2 available
Add to basketBuch. Condition: 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.
-
Kernel Based Algorithms for Mining Huge Data Sets
Published by Springer Berlin Heidelberg, Springer Berlin Heidelberg Nov 2010, 2010
ISBN 10: 3642068561 ISBN 13: 9783642068560
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
£ 94.94
Convert currency£ 30.15 shipping from Germany to United KingdomQuantity: 1 available
Add to basketTaschenbuch. Condition: 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 GmbH, Tiergartenstr. 17, 69121 Heidelberg 276 pp. Englisch.
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Condition: New. Print on Demand pp. 278 96 Illus.
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£ 140.83
Convert currency£ 6.85 shipping from Germany to United KingdomQuantity: 4 available
Add to basketCondition: New. PRINT ON DEMAND pp. 278.
