Rigorous Learning Curve Bounds from Statistical Mechanics

In this paper we introduce and investigate a mathematically rigorous theory of learning curves that is based on ideas from statistical mechanics. The advantage of our theory over the well-established Vapnik-Chervonenkis theory is that our bounds can be considerably tighter in many cases, and are also more re ective of the true behavior (functional form) of learning curves. This behavior can often exhibit dramatic properties such as phase transitions, as well as power law asymptotics not explained by the VC theory. The disadvantages of our theory are that its application requires knowledge of the input distribution, and it is limited so far to nite cardinality function classes. We illustrate our results with many concrete examples of learning curve bounds derived from our theory.

• Type: Conference
• Year: 1994
• Where: COLT
• Authors: David Haussler, H. Sebastian Seung, Michael J. Kearns, Naftali Tishby
• Keywords: COLT 1994, Learning Curve, Machine Learning, Theory, Well-established Vapnik-chervonenkis Theory