SVM Based Fast Classification Using Cascade Feature Selection

• Main Authors: Chun-WeiChen, 陳俊維
• Other Authors: Ming-Der Shieh
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/17709418978061084364

碩士 === 國立成功大學 === 電機工程學系碩博士班 === 100 === Support vector machine (SVM) is a state-of-art large margin classifier that has been applied in many applications. The main issue of developing SVM hardware classifiers is its unlimited support vector memory. The memory size depends on the number of support vectors, which are upper-bounded by the number of training samples. The size of training dataset varies with different applications. Even in the same application like image processing, data granularity is quite different for pixel and video sequence levels. Data granularity is positively proportional to the difficulty of data collection; the difficulty is related to the training dataset size. Many techniques have been proposed to reduce the number of support vector; however, most of them may lead to the degradation in classification accuracy. In this work, we proposed a novel support vector reduction method using cascade feature selection. The complexity is reduced by applying several linear classifiers in dataset segments. Simulation results demonstrate that the proposed algorithm not only reduce the number of support vectors, but also has a comparable accuracy with that of traditional radial basis function (RBF) classifiers.