Kernel based algorithms for mining huge data sets - (E-BOOK) Te-Ming Huang, Vojislav Kecman, Ivica Kopriva.
Series: Studies in computational intelligence, v. 17Publisher: Berlin ; New York : Springer, c2006Description: xvi, 260 p. : ill. ; 25 cmISBN: 3540316817 (acid-free paper); 9783540316817 (acid-free paper)Subject(s): Data mining | Kernel functions | Machine learning | E-BOOKDDC classification: 006.312 Online resources: Publisher description | Table of contents only
Contents:
Introduction.(Page-1)- Support Vector Machines in Classification and Regression An Introduction. (Page-11)- Iterative Single Data Algorithm for Kernel Machines from Huge Data Sets: Theory and Performance. (Page-61)- Feature Reduction with Support Vector Machines and Application in DNA Microarray Analysis. (Page-97)- Semi-supervised Learning and Applications. (Page-125)- Unsupervised Learning by Principal and Independent Component Analysis. (Page-175).
| Item type | Current location | Home library | Collection | Call number | URL | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|---|---|---|
Book
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Military College of Signals (MCS) | Military College of Signals (MCS) | NFIC | 006.312 HUA (Browse shelf) | Link to resource | Available | MCSEB-845 |
Total holds: 0
Introduction.(Page-1)- Support Vector Machines in Classification and Regression An Introduction. (Page-11)- Iterative Single Data Algorithm for Kernel Machines from Huge Data Sets: Theory and Performance. (Page-61)- Feature Reduction with Support Vector Machines and Application in DNA Microarray Analysis. (Page-97)- Semi-supervised Learning and Applications. (Page-125)- Unsupervised Learning by Principal and Independent Component Analysis. (Page-175).
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