Memory-Based Methods for Regression and Classification
Thomas G. Dietterich, Dietrich Wettschereck, Chris Atkeson, Andrew Moore · neural information processing systems · 1993
Memory-based learning methods operate by storing all (or most) of the training data and deferring analysis of that data until run time (i.e., when a query is presented and a decision or prediction must be made). When a query is received, these methods generally answer the query by retrieving and analyzing a small subset of the training data--namely, data in the immediate neighborhood of the query point. In short, memory-based methods are lazy (they wait until the query) and (they use only a local neighborhood). The purpose of this workshop was to review the state-of-the-art in memory-based methods and to understand their relationship to eager and global learning algorithms such as batch backpropagation.