Maxi-min margin machine: Learning large margin classifiers locally and globally
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摘要In this paper, we propose a novel large margin classifier, called the maxi-min margin machine (M-4). This model learns the decision boundary both locally and globally. In comparison, other large margin classifiers construct separating hyperplanes only either locally or globally. For example, a state-of-the-art large margin classifier, the support vector machine (SVM), considers data only locally, while another significant model, the minimax probability machine (MPM), focuses on building the decision hyperplane exclusively based on the global information. As a major contribution, we show that SVM yields the same solution as M-4 when data satisfy certain conditions, and MPM can be regarded as a relaxation model of M-4. Moreover, based on our proposed local and global view of data, another popular model, the linear discriminant analysis, can easily be interpreted and extended as well. We describe the M-4 model definition, provide a geometrical interpretation, present theoretical justifications, and propose a practical sequential conic programming method to solve the optimization problem. We also show how to exploit Mercer kernels to extend M-4 for nonlinear classifications. Furthermore, we perform a series of evaluations on both synthetic data sets and real-world benchmark data sets. Comparison with SVM and MPM demonstrates the advantages of our new model.
著者Huang KZ, Yang HQ, King I, Lyu MR
期刊名稱IEEE Transactions on Neural Networks
出版年份2008
月份2
日期1
卷號19
期次2
出版社IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
頁次260 - 272
國際標準期刊號1045-9227
電子國際標準期刊號1941-0093
語言英式英語
關鍵詞classification; kernel methods; large margin; learning locally and globally; second-order cone programming
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering; Engineering, Electrical & Electronic

上次更新時間 2021-18-02 於 00:44