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Kernel based algorithms for mining huge data sets : supervised, semi-supervised, and unsupervised learning

Kernel based algorithms for mining huge data sets : supervised, semi-supervised, and unsupervised learning (Loan 7 times)

Material type
단행본
Personal Author
Huang, Te-Ming. Kecman, V. (Vojislav) 1948- Kopriva, Ivica 1962-
Title Statement
Kernel based algorithms for mining huge data sets : supervised, semi-supervised, and unsupervised learning / Te-Ming Huang, Vojislav Kecman, Ivica Kopriva.
Publication, Distribution, etc
Berlin ; New York :   Springer ,   c2006.  
Physical Medium
xvi, 260 p. : ill. ; 25 cm.
Series Statement
Studies in computational intelligence , 1860-949X ; v. 17
ISBN
9783540316817 (acid-free paper) 3540316817 (acid-free paper)
Subject Added Entry-Topical Term
Machine learning. Data mining. Kernel functions.
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008 051221s2006 gw a b 000 0 eng d
010 ▼a 2005938947
020 ▼a 9783540316817 (acid-free paper)
020 ▼a 3540316817 (acid-free paper)
035 ▼a (KERIS)REF000012761844
040 ▼a OHX ▼c OHX ▼d BAKER ▼d HNK ▼d DLC ▼d 211009
042 ▼a lccopycat
050 0 0 ▼a Q325.5 ▼b .H83 2006
082 0 0 ▼a 006.3/12 ▼2 22
090 ▼a 006.312 ▼b H874k
100 1 ▼a Huang, Te-Ming.
245 1 0 ▼a Kernel based algorithms for mining huge data sets : ▼b supervised, semi-supervised, and unsupervised learning / ▼c Te-Ming Huang, Vojislav Kecman, Ivica Kopriva.
260 ▼a Berlin ; New York : ▼b Springer , ▼c c2006.
300 ▼a xvi, 260 p. : ▼b ill. ; ▼c 25 cm.
440 0 ▼a Studies in computational intelligence , ▼x 1860-949X ; ▼v v. 17
650 0 ▼a Machine learning.
650 0 ▼a Data mining.
650 0 ▼a Kernel functions.
700 1 ▼a Kecman, V. ▼q (Vojislav) ▼d 1948-
700 1 ▼a Kopriva, Ivica ▼d 1962-
945 ▼a KINS

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Sci-Info(Stacks2)/ Call Number 006.312 H874k Accession No. 121136174 Availability Available Due Date Make a Reservation Service B M

Contents information

Table of Contents

Support Vector Machines in Classification and Regression - An Introduction.- Iterative Single Data Algorithm for Kernel Machines from Huge Data Sets: Theory and Performance.- Feature Reduction with Support Vector Machines and Application in DNA Microarray Analysis.- Semi-supervised Learning and Applications.- Unsupervised Learning by Principal and Independent Component Analysis.


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