Compositional noisy-logical learning

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.

• Type: Conference
• Year: 2009
• Where: ICML
• Authors: Alan L. Yuille, Songfeng Zheng
• Keywords: Conditional Probability Distribution, Fewer Weak Classifiers, ICML 2009, Machine Learning, Well-known Adaboost Algorithm