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DING Shi-fei, QI Bing-juan, TAN Hong-yan. An Overview on Theory and Algorithm of Support Vector Machines[J]. Journal of University of Electronic Science and Technology of China, 2011, 40(1): 2-10. doi: 10.3969/j.issn.1001-0548.2011.01.001
Citation: DING Shi-fei, QI Bing-juan, TAN Hong-yan. An Overview on Theory and Algorithm of Support Vector Machines[J]. Journal of University of Electronic Science and Technology of China, 2011, 40(1): 2-10. doi: 10.3969/j.issn.1001-0548.2011.01.001
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An Overview on Theory and Algorithm of Support Vector Machines

doi: 10.3969/j.issn.1001-0548.2011.01.001
  • 1.

    School of Computer Science and Technology,China University of Mining and Technology Xuzhou Jiangsu 221116;

  • 2.

    Key Laboratory of Intelligent Information Processing,Institute of Computing Technology,Chinese Academy of Science Haidian Beijing 100080;

  • 3.

    High Performance Network Laboratory,Institute of Acoustic,Chinese Academy of Science Haidian Beijing 100190

  • Received Date: 2010-12-15
  • Rev Recd Date: 2011-01-09
  • Publish Date: 2011-02-15
  • Statistical learning theory is the statistical theory of smallsample, and it focuses on the statistical law and the nature of learning of small samples. Support vector machine is a new machine learning method based on statistical learning theory, and it has become the research field of machine learning because of its excellent performance. This paper describes the theoretical basis of support vector machines (SVM) systematically, sums up the mainstream machine training algorithms of traditional SVM and some new learning models and algorithms detailedly, and finally points out the research and development prospects of support vector machine.
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An Overview on Theory and Algorithm of Support Vector Machines

doi: 10.3969/j.issn.1001-0548.2011.01.001
  • 1. School of Computer Science and Technology,China University of Mining and Technology Xuzhou Jiangsu 221116;
  • 2. Key Laboratory of Intelligent Information Processing,Institute of Computing Technology,Chinese Academy of Science Haidian Beijing 100080;
  • 3. High Performance Network Laboratory,Institute of Acoustic,Chinese Academy of Science Haidian Beijing 100190
  • Received Date: 2010-12-15
  • Rev Recd Date: 2011-01-09
  • Publish Date: 2011-02-15

Abstract: Statistical learning theory is the statistical theory of smallsample, and it focuses on the statistical law and the nature of learning of small samples. Support vector machine is a new machine learning method based on statistical learning theory, and it has become the research field of machine learning because of its excellent performance. This paper describes the theoretical basis of support vector machines (SVM) systematically, sums up the mainstream machine training algorithms of traditional SVM and some new learning models and algorithms detailedly, and finally points out the research and development prospects of support vector machine.

DING Shi-fei, QI Bing-juan, TAN Hong-yan. An Overview on Theory and Algorithm of Support Vector Machines[J]. Journal of University of Electronic Science and Technology of China, 2011, 40(1): 2-10. doi: 10.3969/j.issn.1001-0548.2011.01.001
Citation: DING Shi-fei, QI Bing-juan, TAN Hong-yan. An Overview on Theory and Algorithm of Support Vector Machines[J]. Journal of University of Electronic Science and Technology of China, 2011, 40(1): 2-10. doi: 10.3969/j.issn.1001-0548.2011.01.001
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