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Author Huang, Te-Ming.

Title Kernel based algorithms for mining huge data sets : supervised, semi-supervised, and unsupervised learning / Te-Ming Huang, Vojislav Kecman, Ivica Kopriva
Published Berlin : Springer-Verlag, [2006]


Location Call no. Vol. Availability
 MELB  006.31 Hua/Kba  AVAILABLE
Description xvi, 260 pages : illustrations ; 25 cm
Series Studies in computational intelligence, 1860-949X ; v. 17
Studies in computational intelligence. 1860-949X ; v. 17
Contents 1 Introduction -- 2 Support vector machines in classification and regression - an introduction -- 3 Iterative single data algorithm for kernel machines from huge data sets : theory and performance -- 4 Feature reduction with support vector machines and application in DNA microarray analysis -- 5 Semi-supervised learning and applications -- 6 Unsupervised learning by principal and independent component analysis -- A Support vector machines -- B Matlab code for ISDA classification -- C Matlab code for ISDA regression -- D Matlab code for conjugate gradient method with box constraints -- E Uncorrelatedness and independence -- F Independent component analysis by empirical estimation of score functions i.e., probability density functions -- G SemiL user guide
Summary "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 focuses on a broad range of machine learning algorithms in bioinformatics (gene microarrays), text categorization, numerals recognition, as well as in the images and audio signals de-mixing (blind source separation) areas."--BOOK JACKET
Notes New Zealand authors : Te-Ming Huang and Vojislav Kecman of the Faculty of Engineering, University of Auckland
Bibliography Includes bibliographical references (pages [247]-255) and index
Subject Machine learning.
Data mining.
Kernel functions.
Author Kecman, V. (Vojislav), 1948-
Kopriva, Ivica, 1962-
LC no. 2005938947
ISBN 3540316817 (