Learning from Examples and Membership Queries with Structured Determinations

It is well known that prior knowledge or bias can speed up learning, at least in theory. It has proved di cult to make constructive use of prior knowledge, so that approximately correct hypotheses can be learned e ciently. In this paper, we consider a particular form of bias which consists of a set of \determinations." A set of attributes is said to determine a given attribute if the latter is purely a function of the former. The bias is tree-structured if there is a tree of attributes such that the attribute at any node is determined by its children, where the leaves correspond to input attributes and the root corresponds to the target attribute for the learning problem. The set of allowed functions at each node is called the basis. The tree-structured bias restricts the target functions to those representable by a read-once formula (a Boolean formula in which each variable occurs at most once) of a given structure over the basis functions. We show that e cient learning using a ...

• Type: Journal
• Year: 1998
• Where: ML
• Authors: Prasad Tadepalli, Stuart J. Russell
• Keywords: Bias, Correct Hypotheses, Machine Learning, ML 1998, Tree-structured Bias