SUPPORT-VECTOR NETWORKS
SUPPORT-VECTOR NETWORKS
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DOI：
10.1007/bf00994018
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发表时间：
1995-09-01
期刊：
影响因子：
7.5
通讯作者：
VAPNIK, V
中科院分区：
文献类型：
--
作者：
CORTES, C;VAPNIK, V
支持向量网络是一种新的两组分类问题的学习机。该机器在概念上实现了以下思想：输入向量被非线性映射到一个非常高维的特征空间。在这个特征空间中构造一个线性决策曲面。决策面的特殊性质保证了学习机的高泛化能力。支持向量网络背后的思想以前是针对训练数据可以无错误地分离的受限情况实现的。本文将这一结果推广到不可分离的训练数据，证明了利用多项式输入变换的支持向量网络的高泛化能力。我们还比较了支持向量网络的性能，各种经典的学习算法，都参加了光学字符识别的基准研究。
The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.