Title :
A HSC-based sample selection method for support vector machine
Author :
He, Qing ; Li, Ning ; Shi, Zhong-zhi
Author_Institution :
Key Lab. of Intell. Inf. Process., Chinese Acad. of Sci., Beijing, China
Abstract :
Support Vector Machine (SVM) is a classification technique of machine learning based on statistical learning theory. A quadratic optimization problem needs to be solved in the algorithm, and with the increase of the samples, the time complexity will also increase. So it is necessary to shrink training sets to reduce the time complexity. A sample selection method for SVM is proposed in this paper. It is inspired from the Hyper surface classification (HSC), which is a universal classification method based on Jordan Curve Theorem, and there is no need for mapping from lower-dimensional space to higher-dimensional space. The experiments show that the algorithm shrinks training sets keeping the accuracy for unseen vectors high.
Keywords :
learning (artificial intelligence); pattern classification; statistical analysis; support vector machines; HSC-based sample selection method; Jordan curve theorem; high dimensional space; hyper surface classification; low dimensional space; machine learning; quadratic optimization problem; statistical learning theory; support vector machine; time complexity; universal classification method; Accuracy; Classification algorithms; Machine learning; Machine learning algorithms; Support vector machines; Testing; Training; Hyper surface classification; Sample selection; Support vector machines;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-6526-2
DOI :
10.1109/ICMLC.2010.5580974