Towards a Unified Approach to Concept Learning

Proceedings of the AAAI Conference on Artificial Intelligence, 13

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 13

SIGART/AAAI Doctoral Consortium Abstracts

Rule induction (either directly or by means of decision trees) and case-based learning (forms of which are also known as instance-based, memory-based and nearest-neighbor learning) arguably constitute the two leading symbolic approaches to concept and classification learning. Rule-based methods discard the individual training examples, and remember only abstractions formed from them. At performance time, rules are applied by logical match (i.e., only rules whose preconditions are satisfied by an example are applied to it). Case-based methods explicitly memorize some or all of the examples; they avoid forming abstractions, and instead invest more effort at performance time in finding the most similar cases to the target one.