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Authors: Krzysztof Sopyla and Pawel Drozda

Affiliation: University of Warmia and Mazury, Poland

ISBN: 978-989-758-018-5

Keyword(s): SVM Classification, GPU Computing, Sparse Data Formats, SVM Kernels.

Related Ontology Subjects/Areas/Topics: Classification ; Kernel Methods ; Large Margin Methods ; Pattern Recognition ; Theory and Methods

Abstract: This paper presents the ongoing research on the GPU SVM solutions for classification of big sparse datasets. In particular, after the success of implementation of RBF kernel for sparse matrix formats in previous work we decided to evaluate Chi2 and Exponential Chi2 kernels. Moreover, the details of GPU solver are pointed. Experimental session summarizes results of GPU SVM classification for different sparse data formats and different SVM kernels and demonstrates that solution for Exponential Chi2 achieves significant accelerations in GPU SVM processing, while the results for Chi2 kernel are very far from satisfactory.

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Paper citation in several formats:
Sopyla, K. and Drozda, P. (2014). GPU Solver with Chi-square Kernels for SVM Classification of Big Sparse Problems.In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 331-336. DOI: 10.5220/0004922603310336

@conference{icpram14,
author={Krzysztof Sopyla. and Pawel Drozda.},
title={GPU Solver with Chi-square Kernels for SVM Classification of Big Sparse Problems},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={331-336},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004922603310336},
isbn={978-989-758-018-5},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - GPU Solver with Chi-square Kernels for SVM Classification of Big Sparse Problems
SN - 978-989-758-018-5
AU - Sopyla, K.
AU - Drozda, P.
PY - 2014
SP - 331
EP - 336
DO - 10.5220/0004922603310336

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