A novel parallel reduced support vector machine

Authors:
Fangfang Wu;Yinliang Zhao;Zefei Jiang
Affiliations:
Institute of Neocomputer, Xi'an Jiaotong University, Xi'an, People's Republic of China;Institute of Neocomputer, Xi'an Jiaotong University, Xi'an, People's Republic of China;Institute of Neocomputer, Xi'an Jiaotong University, Xi'an, People's Republic of China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

Citing 8
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models

Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Relationship between support vector set and kernel functions in SVM

Journal of Computer Science and Technology
Training Support Vector Machines: an Application to Face Detection

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)

Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
A fast parallel optimization for training support vector machine

MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers

IEEE Transactions on Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Support Vector Machine (SVM) has been applied in many classification systems successfully. However, it is restricted to work well on the small sample sets. This paper presents a novel parallel reduced support vector machine. The proposed algorithm consists of three parts: firstly dividing the training samples into some grids; then training sample subset through density clustering; and finally classifying the samples. After clustering the positive samples and negative samples, this algorithm picks out such samples that locate on the edge of clusters as reduced sample subset. Then, we sum up these reduced sample subsets as reduced sample set. These reduced samples are then used to find the support vectors and the optimal classifying hyperplane by support vector machine. Additionally, it also improves classification precision by reducing the percentage of counterexamples in kernel object ε-area. Experiment results show that not only efficiency but also classification precision are improved, compared with other algorithms.