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Building support vector machines with reduced classifier complexity
Building support vector machines with reduced classifier complexity
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机译:构建具有减少分类器复杂性的支持向量机
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摘要
Support vector machines (SVMs), though accurate, are not preferred in applications requiring great classification speed, due to the number of support vectors being large. To overcome this problem a primal system and method with the following properties has been devised: (1) it decouples the idea of basis functions from the concept of support vectors; (2) it greedily finds a set of kernel basis functions of a specified maximum size (dmax) to approximate the SVM primal cost function well; (3) it is efficient and roughly scales as O(ndmax2) where n is the number of training examples; and, (4) the number of basis functions it requires to achieve an accuracy close to the SVM accuracy is usually far less than the number of SVM support vectors.
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机译:虽然支持向量机(SVM)准确,但由于支持向量的数量很大,因此在要求快速分类的应用程序中并不是首选。为了克服这个问题,设计了一种具有以下特性的原始系统和方法:(1)将基函数的概念与支持向量的概念分离; (2)贪婪地找到一组指定最大大小(d max Sub>)的内核基函数,以很好地近似SVM原始成本函数; (3)它是有效的,并且可以大致缩放为O(nd max Sub> 2 Sup>),其中n是训练示例的数量; (4)实现接近SVM精度的精度所需的基本函数数通常远小于SVM支持向量的数量。
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