A novel and quick SVM-based multi-class classifier
A novel and quick SVM-based multi-class classifier
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DOI：
10.1016/j.patcog.2006.05.034
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发表时间：
2006-11-01
影响因子：
8
通讯作者：
Cao, Liping
中科院分区：
文献类型：
--
作者：
Liu, Yiguang;You, Zhisheng;Cao, Liping
用不同的真实的正数pi来表示各种模式类别，通过非线性映射将输入的模式映射到一个特殊的特征空间中，假设映射后的模式与数pi之间存在线性关系，其偏差和系数都是未知的，并将该线性关系的零输出对应的超平面作为基超平面。为了确定待定参数，建立目标函数，旨在最小化属于同一类型的模式的输出与对应的pi之间的差，并且最大化对应于不同模式类型的任意两个不同超平面之间的距离。由于目标函数的形式与支持向量回归机的目标函数相同，因此线性关系的系数和偏差可以通过一些已知的方法如SVMlight方法来计算。同时，给出了三种确定pi的方法，其中最好的方法是在训练过程中确定，具有较高的精度。在IRIS数据集上的实验结果表明，该方法的分类精度优于许多基于SVM的多类分类器，接近DAGSVM（decision-Directed acyclic graph SVM），且识别速度最快。(c)2006年由Elsevier Ltd代表Pattern Recognition Society出版。
Use different real positive numbers pi to represent all kinds of pattern categories, after mapping the inputted patterns into a special feature space by a non-linear mapping, a linear relation between the mapped patterns and numbers pi is assumed, whose bias and coefficients are undetermined, and the hyper-plane corresponding to zero output of the linear relation is looked as the base hyper-plane. To determine the pending parameters, an objective function is founded aiming to minimize the difference between the outputs of the patterns belonging to a same type and the corresponding pi, and to maximize the distance between any two different hyper-planes corresponding to different pattern types. The objective function is same to that of support vector regression in form, so the coefficients and bias of the linear relation are calculated by some known methods such as SVMlight approach. Simultaneously, three methods are also given to determine pi, the best one is to determine them in training process, which has relatively high accuracy. Experiment results of the IRIS data set show that, the accuracy of this method is better than those of many SVM-based multi-class classifiers, and close to that of DAGSVM (decision-directed acyclic graph SVM), emphatically, the recognition speed is the highest. (c) 2006 Published by Elsevier Ltd on behalf of Pattern Recognition Society.