Using Linear Independence and Error Estimation for Online Support Vector Machine

• Main Authors: Yu-Siang Houng, 黃郁翔
• Other Authors: Chih-Chia Yao
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/18708143242984682776

碩士 === 朝陽科技大學 === 資訊工程系碩士班 === 101 === This thesis presents a novel on line learning algorithm for support vector machines. This algorithm contains three stages learning mechanism. In first stage the property of linear independent is used to extract the support vector. Then, in second stage the parameters of support vector machines are quickly obtained and the amount of classification error is calculated by using Least Square Support Vector Machines. The third learning stage recalculates the parameter of support vector machines to improve the performance of support vector machines. However, the third stage is started depending on whether the amount of classification error is exceed the threshold or not. In third stage part of the training patterns are selected as support vector and the model of support vector machines is re-established by using Weighted Least Square Support Vector Machines. Experimental results prove that our proposed algorithm significantly reduce the required number of support vectors and maintain a certain accuracy.