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Discrete Quasi-gradient Features Weighting Algorithm

机译:离散准梯度特征加权算法

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摘要

A new method of feature weighting, useful also for feature extraction has been described. It is quite efficient and gives quite accurate results. Weighting algorithm may be used with any kind of learning algorithm. The weighting algorithm with k-nearest neighbors model was used to estimate the best feature base for a given distance measure. Results obtained with this algorithm clearly show its superior performance in several benchmark tests.
机译:已经描述了一种新的特征加权方法,也已经描述了用于特征提取。 它非常有效,并提供了非常准确的结果。 加权算法可以与任何类型的学习算法一起使用。 使用K-CORMATE邻居模型的加权算法用于估计给定距离测量的最佳特征群。 通过该算法获得的结果清楚地显示了几种基准测试中的卓越性能。

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