PBL: Prototype-Based Learning Algorithm

Kuniaki Uehara, Masayuki Tanizawa and Sadao Maekawa

Department of Computer Science and Systems Engineering
Kobe University
Nada, Kobe 657, Japan


Abstract. In this paper, we will introduce an inductive learning algorithm 
called Prototype-Based Learning (PBL). PBL learns a concept
description, which consists of both prototypical attributes and attribute
importances, by using a distance metric based on prototype-theory and
information-theory. PBL can learn the concept description from even a
small set of training cases and is tolerant of inappropriate cases. Furthermore, 
even the attribute importance differs depending on the combinations of the 
other attribute-value pairs present describing the case,
PBL can learn the concept description and highly utilize it so as to do
the accurate classification. Finally, PBL can learn indexing knowledge
directly from the concept description, which is useful for a human expert 
to understand and verify the concept description generated by the
learning algorithm.
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