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2018 | OriginalPaper | Buchkapitel

A Modification of Solution Optimization in Support Vector Machine Simplification for Classification

verfasst von : Pham Quoc Thang, Nguyen Thanh Thuy, Hoang Thi Lam

Erschienen in: Information Systems Design and Intelligent Applications

Verlag: Springer Singapore

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Abstract

The efficient classification ability of support vector machine (SVM) has been shown in many practical applications, but currently it is significantly slower in testing phase than other classification approaches due to large number of support vectors included in the solution. Among different approaches, simplification of support vector machine (SimpSVM) accelerates the test phase by replacing original SVM with a simplified SVM that uses significantly fewer support vectors. Nevertheless, the final aim of the simplification is to try keeping the simplified solution as similar as possible to the original one. To ameliorate this similarity, in this paper, we present a modification of solution optimization in SimpSVM. The proposed approach is based on stochastic optimization. Experiments on benchmark and sign language datasets show improved results by our modification.

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Titel: A Modification of Solution Optimization in Support Vector Machine Simplification for Classification
verfasst von: Pham Quoc Thang
Nguyen Thanh Thuy
Hoang Thi Lam
Verlag: Springer Singapore
Buch: Information Systems Design and Intelligent Applications
Print ISBN: 978-981-10-7511-7

Electronic ISBN: 978-981-10-7512-4

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-981-10-7512-4_15

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