Aspects of discrete mathematics and probability in the theory of machine learning

Anthony, Martin ORCID: 0000-0002-7796-6044 (2008) Aspects of discrete mathematics and probability in the theory of machine learning. Discrete Applied Mathematics, 156 (6). pp. 883-902. ISSN 0166-218X

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Abstract

This paper discusses the applications of certain combinatorial and probabilistic techniques to the analysis of machine learning. Probabilistic models of learning initially addressed binary classification (or pattern classification). Subsequently, analysis was extended to regression problems, and to classification problems in which the classification is achieved by using real-valued functions (where the concept of a large margin has proven useful). Another development, important in obtaining more applicable models, has been the derivation of data-dependent bounds. Here, we discuss some of the key probabilistic and combinatorial techniques and results, focusing on those of most relevance to researchers in discrete applied mathematics.

Item Type:	Article
Official URL:	http://www.sciencedirect.com/science/journal/01662...
Additional Information:	© 2008 Elsevier
Divisions:	Mathematics
Subjects:	Q Science > QA Mathematics
Date Deposited:	18 Feb 2009 10:50
Last Modified:	01 Nov 2024 04:19
URI:	http://eprints.lse.ac.uk/id/eprint/22719

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