Title :
An study on the effect of learning parameters for inducing compact SVM
Author :
Kaneda, Yuya ; Zhao, Qiangfu ; Watarai, Kyohei
Author_Institution :
Dept. of Comput. & Inf. Syst., Univ. of Aizu, Aizu-Wakamatsu, Japan
Abstract :
Support vector machine (SVM) is one of the best machine learning models that offers high accuracy both for recognition and for regression. One drawback of using SVM is that the system implementation cost is usually proportional to the number of training data and the dimension of the feature space. Therefore, it is difficult to use SVM in mobile devices such as IC cards and smart phones. In our study, we have tried to solve the problem using dimensionality reduction (DR). Since implementation cost of DR should also be considered in a restricted computing environment, we adopted a simple centroid based DR method. In this paper, we investigate the effect of learning parameters on the performance of the system, and provide some insights on obtaining compact SVMs.
Keywords :
learning (artificial intelligence); support vector machines; centroid based DR method; compact SVM; dimensionality reduction; feature space; learning parameters; machine learning models; mobile devices; support vector machine; system implementation cost; system performance; training data; Breast cancer; Databases; Error analysis; Kernel; Machine learning; Support vector machines; Training data; Centroid Based Dimensionality Reduction; Decision Surface Mapping; Dimensionality Reduction; Machine Learning; Support Vector Machine;
Conference_Titel :
Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on
Conference_Location :
Seoul
Print_ISBN :
978-1-4673-1713-9
Electronic_ISBN :
978-1-4673-1712-2
DOI :
10.1109/ICSMC.2012.6377924
