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Lecture Notes in Computer Science
This paper deals with an unusual phenomenon where most machine learning algorithms yield good performance on the training set but systematically worse than random performance on the test set. This has been observed so far for some natural data sets and demonstrated for some synthetic data sets when the classification rule is learned from a small set of training samples drawn from some high dimensional space. The initial analysis presented in this paper shows that anti-learning is a property ofdoi:10.1007/11564089_8 fatcat:v32mzx4xxffyzp3sz6kcvfmdn4