A method of type recognition using probabilistic constraint support vector machine

Man Jie
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Abstract

A new support vector machine classifier for recognition of vehicle type which has been captured from traffic scene images. A new support vector machine classifier is presented with probabilistic constrains which presence probability of samples in each class is determined based on a distribution function. Noise is caused to found incorrect support vectors thereupon margin can not be maximized. In the proposed method, constraints boundaries and constraints occurrence have probability density functions which it helps for achieving maximum margin.
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一种基于概率约束支持向量机的类型识别方法
一种新的支持向量机分类器用于从交通场景图像中提取的车辆类型识别。提出了一种基于概率约束的支持向量机分类器,通过分布函数确定每一类样本的存在概率。噪声会导致发现不正确的支持向量,从而使余量不能最大化。在该方法中,约束边界和约束发生具有概率密度函数,有助于实现最大裕度。
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