Solving selection problems combining relational learning and Bayesian learning
Tomofumi Nakano, Nobuhiro Inuzuka · International Journal of Knowledge-based and Intelligent Engineering Systems · 2007
This paper defines a selection problem where an appropriate object is selected from a set that is specified by parameters. We discuss inductive learning of selection problems and proposed a method combining inductive logic programming (ILP) and Bayesian learning. Our methods estimate probability of each choice by evaluating likelihood of an induced relation from an ideal binary relation. Bayesian learning combines a prior probability of objects and the estimated probability. By making several assumptions on probability estimation, we give several methods. The methods are applied to Part-of-Speech tagging.