Paper Titles

The Application of Data Mining Technology in the Customer Churn Prewarning
p.2198

The Application of DES Encryption and Decryption Algorithm in the STS Protocol
p.2202

A Kind of Self-Constructed Category Dictionary in Chinese Text Classification
p.2206

Multivariate Principal Component Analysis for Production and Energy Consumption of Cutter Suction Dredger
p.2211

An Improved Grid Search Algorithm for Parameters Optimization on SVM
p.2216

Adaptive Algorithm for Determination of Optimal Wavelet Decomposition Level Based on Jarque-Bera Test
p.2220

The Equivalence Relations Between Two Fuzzy Implications
p.2224

Study on the Determination of the Separated Layer Water Injection Based on the Grey Synthetic Degree
p.2227

Research of MPC Method on Preventing PIOs and Comparison with other Methods
p.2231

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 644-650An Improved Grid Search Algorithm for Parameters...

An Improved Grid Search Algorithm for Parameters Optimization on SVM

Article Preview

Abstract:

The algorithm in this paper is based on the basic principle of SVM. The sale data classification SVM classifier is designed using this algorithm. Also three classifiers including traditional grid search algorithm, ZGenetic Algorithm and Particle Swarm Optimization are used to do the comparison experiments of classification. Result shows that the improved grid-search algorithm can reduce the SVM classifier’s computational complexity effectively and improve its performance and classification accuracy.

You might also be interested in these eBooks

Machine Tool Technology, Mechatronics and Information Engineering

Info:

Periodical:

Applied Mechanics and Materials (Volumes 644-650)

Pages:

2216-2219

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.644-650.2216

Citation:

Cite this paper

Online since:

September 2014

Authors:

Chao Liu, Shi Qun Yin*, Miao Zhang, Yi Zeng, Jiang Yue Liu

Keywords:

Genetic Algorithm (GA), Grid-Search, Parameter Selection, Particle Swarm Optimization (PSO), Support Vector Machine (SVM)

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

* - Corresponding Author

[1] Raftery, A. E. and Dean, N. Journal of American Statistical Association, 101(473): 168-178, (2006).

[2] Schwarz, G. Estimating the dimension of a model. The Annals of Statistics, 6(2): 461-464, (2008).

[3] Steinley, D. and Brusco, M. J. Psychometrika, 73(1): 125-144, (2008).

[4] Strehl, A. and Ghosh, J. Journal of Machine Learning Research, 3: 583-617, (2012).

[5] Zeng, H. and Cheung, Y. -M. Pattern Recognition, 42: 243-250, (2009).

[6] Zhang, N. L. Journal of Machine Learning Research, 5: 697-723, (2011).

[7] C. -C. Chang, C. -J. Lin, LIBSVM: a library for support vector machines, 2010. <http: /www. csie. ntu. edu. tw/cjlin/libsvm>.

[8] McLachlan, G. J. and Peel, D. Finite Mixture Models. Wiley, New York, (2000).

[9] H. Drucker, D. Wu, V.N. Vapnik, IEEE Transactions on Neural Networks 10 (2009) 1048–1054.

[10] T. Fawcett, An introduction to ROC analysis, Pattern Recognition Letters 27 (2010) 861-874.

DOI: 10.1016/j.patrec.2005.10.010

[11] G. Forman, Journal of Machine Learning Research 3 (2007) 1289–1305.

[12] Cont, A. Schwarz, D., International Computer Music Conference, p.241–248, (2013).

[13] Dannenberg, R. 1988 International Computer Music Conference, p.243–249, (2008).

[14] Dannenberg, Roger and Mont-Reynaud, Bernard. Following an improvisation in real time. In Proc. of the1987 International Computer Music Conference, p.241–248, (1987).

[15] Flanagan, J. L. and Golden, R. M. Phase vocoder. Bell Systems Technical Journal, 45(Nov): 1493–1509, (2012).

DOI: 10.1002/j.1538-7305.1966.tb01706.x

[16] Franklin, Judy. Improvisation and learning. In Advances in Neural Information Processing Systems 14. MIT Press, Cambridge, MA, (2011).

[17] Li, Y., Dong, M., and Hua, J. IEEE Transaction on Pattern Analysis and Machine Intelligence, 31(5): 953-960, (2009).

[18] Liu, J. S., Zhang, J. L., Palumbo, M. J. Bayesian Statistics, 7: 249-275, (2012).