원문정보
보안공학연구지원센터(IJGDC)
International Journal of Grid and Distributed Computing
Vol.9 No.9
2016.09
pp.129-136
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
A multiple smooth model is proposed by smoothing technique and piecewise technique for large scale data. Mapping the training data to the hidden space with a hidden function, the proposed model divides the original data into several subclasses by Fuzzy C Means (FCM), whose initial cluster centers are selected by samples with large density indexes; derives the smooth differentiable model by utilizing the entropy function to replace the plus function of the slack vector, and introduces linking rules to combine results of subclasses. Simulations demonstrate that the obtained algorithm maintains good classification accuracies, reduces the training time and hardly varies with kernel parameters.
목차
Abstract
1. Introduction
2. MSSVM-FCM: Multiple Smooth Support Vector Machine with FCM Clustering in Hidden Space
2.1 Hidden Space
2.2 FCM clustering
2.3 Multiple Smooth Models in Hidden Space
2.4 Linking Rule
3. Experiments and Comparisons
3.1 Performances Variances with the Class Number
3.2 Performances Variances with the Kernel Width
3.3 Performances on Large Scale Data
4. Conclusions
Acknowledgment
References
1. Introduction
2. MSSVM-FCM: Multiple Smooth Support Vector Machine with FCM Clustering in Hidden Space
2.1 Hidden Space
2.2 FCM clustering
2.3 Multiple Smooth Models in Hidden Space
2.4 Linking Rule
3. Experiments and Comparisons
3.1 Performances Variances with the Class Number
3.2 Performances Variances with the Kernel Width
3.3 Performances on Large Scale Data
4. Conclusions
Acknowledgment
References
저자정보
참고문헌
자료제공 : 네이버학술정보