Self Bounding Learning Algorithms

Most of the work which attempts to give bounds on the generalization error of the hypothesis generated by a learning algorithm is based on methods from the theory of uniform convergence. These bounds are a-priori bounds that hold for any distribution of examples and are calculated before any data is observed. In this paper we propose a different approach for bounding the generalization error after the data has been observed. A self-bounding learning algorithm is an algorithm which, in addition to the hypothesis that it outputs, outputs a reliable upper bound on the generalization error of this hypothesis. We first explore the idea in the statistical query learning framework of Kearns [10]. After that we give an explicit self bounding algorithm for learning algorithms that are based on local search.

• Type: Conference
• Year: 1998
• Where: COLT
• Authors: Yoav Freund
• Keywords: COLT 1998, Generalization Error, Learning Algorithms, Machine Learning, Self-bounding Learning Algorithm