一类使用隐式多项式曲面拟合的分类

A. Erçil, Burak Büke
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引用次数: 1

摘要

当训练集中的对象数量小于所使用的特征数量时,大多数分类过程无法找到良好的分类边界。本文介绍了一种新的方法来解决一类分类问题,该方法是基于隐式多项式曲面拟合特征点云来对我们试图与其他类别分离的一类进行建模。
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One class classification using implicit polynomial surface fitting
When the number of objects in the training set is too small for the number of features used, most classification procedures cannot find good classification boundaries. In this paper, we introduce a new technique to solve the one class classification problem based on fitting an implicit polynomial surface to the point cloud of features to model the one class which we are trying to separate from the others.
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