Compositional noisy-logical learning

Authors

Alan Yuille
Songfeng Zheng, Missouri State UniversityFollow

Abstract

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.

Department(s)

Mathematics

Document Type

Conference Proceeding

DOI

https://doi.org/10.1145/1553374.1553528

Publication Date

9-15-2009

Recommended Citation

Yuille, Alan, and Songfeng Zheng. "Compositional noisy-logical learning." In Proceedings of the 26th Annual International Conference on Machine Learning, pp. 1209-1216. 2009.

Journal Title

ACM International Conference Proceeding Series