Compositional Noisy-Logical Learning
published: Aug. 26, 2009, recorded: June 2009, views: 90
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We describe a new method for learning the conditional probability distribution of a binary-valued variable from labelled training examples. Our proposed Compositional Noisy-Logical Learning (CNLL) approach learns a noisy-logical distribution in a compositional manner. CNLL is an alternative to the well-known AdaBoost algorithm which performs coordinate descent on an alternative error measure. We describe two CNLL algorithms and test their performance compared to AdaBoost on two types of problem: (i) noisy-logical data (such as noisy exclusive-or), and (ii) four standard datasets from the UCI repository. Our results show that we outperform AdaBoost while using signiﬁcantly fewer weak classiﬁers, thereby giving a more transparent classiﬁer suitable for knowledge extraction.
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