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Substantial Improvements in the Set-Covering Projection Classifier CHIRP (Composite Hypercubes on Iterated Random Projections)

机译:集覆盖投影分类器CHIRP(迭代随机投影上的复合超立方体)的实质性改进

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摘要

In Wilkinson et al. [2011] we introduced a new set-covering random projection classifier that achieved average error lower than that of other classifiers in the Weka platform. This classifier was based on an L~∞ norm distance function and exploited an iterative sequence of three stages (projecting, binning, and covering) to deal with the curse of dimensionality, computational complexity, and nonlinear separability. We now present substantial changes that improve robustness and reduce training and testing time by almost an order of magnitude without jeopardizing CHIRP's outstanding error performance.
机译:在威尔金森等人。 [2011]我们引入了一种新的集覆盖随机投影分类器,该分类器的平均误差低于Weka平台中的其他分类器。该分类器基于L〜∞范数距离函数,并利用三个阶段(投影,合并和覆盖)的迭代序列来处理维数,计算复杂性和非线性可分离性的诅咒。现在,我们提出了可改进鲁棒性的实质性更改,并将培训和测试时间减少了近一个数量级,而又不会损害CHIRP出色的错误性能。

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