Browse > Article

Concurrent Support Vector Machine Processor  

위재우 (인하대 공대 전기공학과)
이종호 (인하대 공대 정보통신공학부)
Publication Information
The Transactions of the Korean Institute of Electrical Engineers D / v.53, no.8, 2004 , pp. 578-584 More about this Journal
Abstract
The CSVM(Current Support Vector Machine) that is a digital architecture performing all phases of recognition process including kernel computing, learning, and recall of SVM(Support Vector Machine) on a chip is proposed. Concurrent operation by parallel architecture of elements generates high speed and throughput. The classification problems of bio data having high dimension are solved fast and easily using the CSVM. Quadratic programming in original SVM learning algorithm is not suitable for hardware implementation, due to its complexity and large memory consumption. Hardware-friendly SVM learning algorithms, kernel adatron and kernel perceptron, are embedded on a chip. Experiments on fixed-point algorithm having quantization error are performed and their results are compared with floating-point algorithm. CSVM implemented on FPGA chip generates fast and accurate results on high dimensional cancer data.
Keywords
SVM(Support Vector Machine); FPGA; Gene Expression Data;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Furey T.S., Cristianini N., Duffy N., Bedharski D.W., Schummer M. and Haussler D., 'Support vector machine classification and validation of cancer tissue samples using microarray expression data', Bioinformatics, Vol. 16, No. 10, pp. 906-914, 2000   DOI   ScienceOn
2 Genov, R. and Cauwenberghs, G., 'Kerneltron: support vector 'machine' in silicon', IEEE Transactions on Neural Networks, Vol. 14, No. 5, pp.1426-1434, 2003   DOI   ScienceOn
3 D. Anguita, A. Boni and S. Ridella, 'Digital kernel perceptron', Electronics Letters, Vol. 38, No. 10, pp.445-446, 2002   DOI   ScienceOn
4 T.T. Friess, N. Cristianini and C. Campbell, 'The kernel-adatron algorithm: a fast and simple learning procedure for support vector machines', 15th International Conference on Machine Learning, Wisconsin, USA, pp. 188-196, July 1998
5 B. Scholkopf and A. J. Smola, Learning with kernels: support vector machines, regularization, optimization, and beyond, MIT Press, 2002
6 Vapnik V.N., Statistical Learning Theory, John Wiley and Sons, New Work, 1998
7 T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. caligiuri, C. D. Bloomfield and E. S. Lander, 'Molecular classification of cancer: class discovery and class prediction by gene expression monitoring', Science, Vol. 286, pp. 531-537, 1999   DOI   ScienceOn
8 S. Dudoit, J. Fridlyand and T. P. Speed, 'Comparison of discrimination methods for the classification of tumors using gene expression data', Journal of the American Statistical Association, Vol. 97, No. 457, pp. 77-87, 2002   DOI   ScienceOn
9 Nello C. and John S. T., An Introduction to Support Vector Machine, Cambridge University Press, 2000
10 D. Anguita, A. Boni and S. Ridella, 'A digital architecture for support vector machines: theory, algorithm and FPGA implementation', IEEE Transactions on Neural Networks, Vol. 14, No. 5, pp. 993-1009, 2003   DOI   ScienceOn