S. Natarajan, G. Kunapuli, C. O'Reilly, R.Maclin, T. Walker, D. Page & J.Shavlik (2009).
ILP for Bootstrapped Learning: A Layered Approach to Automating the ILP Setup Problem. Presented at the Nineteenth Conference on Inductive Logic Programming, Leuven, Belgium.

This publication is available in PDF.

Abstract:

This paper introduces a new type of application for ILP called Bootstrapped Learning (BL). BL brings several challenges to ILP, including the need to (a) automate the 'ILP setup' problem, (b) exploit the fact that a well-meaning teacher is providing pedagogically chosen examples and may be offering hints, (c) deal with small numbers of training examples and sometimes no explicit negative examples; and (d) 'bootstrap', i.e., to automatically base learning in part on the results of earlier learning sessions.

Return to the publications of the Univ. of Wisconsin Machine Learning Research Group.