Margin Analysis of the LVQ Algorithm
Margin Analysis of the LVQ Algorithm
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发表时间：
2002
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通讯作者：
K. Crammer;Ran Gilad-Bachrach;A. Navot;Naftali Tishby
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作者：
K. Crammer;Ran Gilad-Bachrach;A. Navot;Naftali Tishby
基于原型的算法通常用于降低最近邻（NN）分类器的计算复杂度。在本文中，我们将讨论这些算法的理论和算法方面。在理论方面，我们提出了基于边缘的泛化界限，这表明这类分类器可以更准确的1-NN规则。此外，我们推导出一个训练算法，选择一组良好的原型使用大利润原则。我们还表明，20岁的学习矢量量化（LVQ）算法自然出现在我们的框架。
Prototypes based algorithms are commonly used to reduce the computational complexity of Nearest-Neighbour (NN) classifiers. In this paper we discuss theoretical and algorithmical aspects of such algorithms. On the theory side, we present margin based generalization bounds that suggest that these kinds of classifiers can be more accurate then the 1-NN rule. Furthermore, we derived a training algorithm that selects a good set of prototypes using large margin principles. We also show that the 20 years old Learning Vector Quantization (LVQ) algorithm emerges naturally from our framework.