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2015 | OriginalPaper | Buchkapitel

Massive Classification with Support Vector Machines

verfasst von : Thanh Nghi Do, Hoai An Le Thi

Erschienen in: Transactions on Computational Collective Intelligence XVIII

Verlag: Springer Berlin Heidelberg

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Abstract

The new boosting of Least-Squares SVM (LS-SVM), Proximal SVM (PSVM), Newton SVM (NSVM) algorithms aim at classifying very large datasets on standard personal computers (PCs). We extend the PSVM, LS-SVM and NSVM in several ways to efficiently classify large datasets. We developed a row incremental version for datasets with billions of data points. By adding a Tikhonov regularization term and using the Sherman-Morrison-Woodbury formula, we developed new algorihms to process datasets with a small number of data points but very high dimensionality. Finally, by applying boosting including AdaBoost and Arcx4 to these algorithms, we developed classification algorithms for massive, very-high-dimensional datasets. Numerical test results on large datasets from the UCI repository showed that our algorithms are often significantly faster and/or more accurate than state-of-the-art algorithms LibSVM, CB-SVM, SVM-perf and LIBLINEAR.

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Vorheriges Kapitel Identification of Underestimated and Overestimated Web Pages Using PageRank and Web Usage Mining Methods
Nächstes Kapitel On a Multi-agent Distributed Asynchronous Intelligence-Sharing and Learning Framework
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Metadaten
Titel
Massive Classification with Support Vector Machines
verfasst von
Thanh Nghi Do
Hoai An Le Thi
Copyright-Jahr
2015
Verlag
Springer Berlin Heidelberg
Buch
Transactions on Computational Collective Intelligence XVIII

Print ISBN: 978-3-662-48144-8

Electronic ISBN: 978-3-662-48145-5

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-662-48145-5_8