Storing and using specific instances improves the performance of several supervised
learning algorithms. These include algorithms that learn decision trees, classification rules …

Abstract SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network)
is a model of how humans learn categories from examples. SUSTAIN initially assumes a …

This paper contains a brief overview of Case‐Based Reasoning (CBR) with an emphasis on
European activities in the field. The main objective was to have a balance between brevity …

Incremental variants of the nearest neighbor algorithm are a potentially suitable choice for
incremental learning tasks. They have fast learning rates, low updating costs, and have …

Instance‐based representations have been applied to numerous classification tasks with
some success. Most of these applications involved predicting a symbolic class based on …

A surprisingly large number of research disciplines have contributed towards the
development of knowledge on lazy problem solving, which is characterized by its storage of …

Case-based learning CBL algorithms are CBR systems that focus on the topic of learning.
This paper notes why CBL algorithms are good choices for many supervised learning tasks …

This handbook provides information about fundamental aspects of the field and explores the
myriad applications of fuzzy logic techniques and methods. It presents basic conceptual …

Several published reports show that instancebased learning algorithms yield high
classification accuracies and have low storage requirements during supervised learning …

This dissertation introduces a framework for specifying instance-based algorithms that can
solve supervised learning tasks. These algorithms input a sequence of instances and yield a …