Comparative efficiency of algorithms based on support vector machines for binary classification

被引：3
|
作者：
Kadyrova N.O. [1 ]
Pavlova L.V. [1 ]
机构：
[1] Institute of Applied Mathematics and Mechanics, St. Petersburg State Polytechnical University, ul. Politekhnicheskaya 29, St. Petersburg
来源：
Biophysics | 2015年 / 60卷 / 1期
关键词：
binary classification; comparative efficiency of support vector classifiers; kernel functions; support vector machine; SVM algorithms;
D O I：
10.1134/S0006350915010145
中图分类号：
学科分类号：
摘要：
Methods of construction of support vector machines (SVMs) require no additional a priori information and allow large volumes of multidimensional data to be processed, which is especially important for solving various problems in computational biology. The main algorithms of SVM construction for binary classification are reviewed. The issue of the quality of the SVM learning algorithms is considered, and a description of proposed algorithms is given that is sufficient for their practical implementation. Comparative analysis of the efficiency of support vector classifiers is presented. © 2015, Pleiades Publishing, Inc.
引用
收藏
页码：13 / 24
页数：11
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