An study on the effect of learning parameters for inducing compact SVM
Yuya Kaneda, Qiangfu Zhao, Kyohei Watarai · 2012
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.