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Springer 2006 2006 ed. Hardcover Good Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
ISBN10: 3540316817, ISBN13: 9783540316817, [publisher: Springer] Hardcover Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority! [Dallas, TX, U.S.A.] [Publication Year: 2006]
ISBN10: 3540316817, ISBN13: 9783540316817, [publisher: Springer] Hardcover New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. [Irving, TX, U.S.A.] [Publication Year: 2006]
ISBN10: 3540316817, ISBN13: 9783540316817, [publisher: Springer Berlin Heidelberg] Hardcover 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. [Einbeck, Germany] [Publication Year: 2006] ...
ISBN10: 3540316817, ISBN13: 9783540316817, [publisher: Springer Berlin Heidelberg] Hardcover 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. [Einbeck, Germany] [Publication Year: 2006] ...
Hard Cover. New. New Book; Fast Shipping from UK; Not signed; Not First Edition; The Kernel Based Algorithms for Mining Huge Data Sets : Supervised, Semi-supervised, and Unsupervised Learning. ISBN 3540316817 9783540316817 [GB]
Hardback. New. 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. ISBN 3540316817 9783540316817 [GB]
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