Learning Recursive Concepts with Anomalies

This paper provides a systematic study of inductive inference of indexable concept classes in learning scenarios in which the learner is successful if its final hypothesis describes a finite variant of the target concept – henceforth called learning with anomalies. As usual, we distinguish between learning from only positive data and learning from positive and negative data. We investigate the following learning models: finite identification, conservative inference, set-driven learning, and behaviorally correct learning. In general, we focus our attention on the case that the number of allowed anomalies is finite but not a priori bounded. However, we also present a few sample results that affect the special case of learning with an a priori bounded number of anomalies. We provide characterizations of the corresponding models of learning with anomalies in terms of finite tell-tale sets. The varieties in the degree of recursiveness of the relevant tell-tale sets observed are alr...

• Type: Conference
• Year: 2000
• Where: ALT
• Authors: Gunter Grieser, Steffen Lange, Thomas Zeugmann
• Keywords: ALT 2000, Correct Learning, Incremental Learning, Machine Learning, Set-driven Learning