Sequential Inductive Learning

In this paper I advocate a new model for inductive learning. Called sequential induction, this model bridges classical fixed-sample learning techniques (which are efficient but ad hoc), and worst-case approaches (which provide strong statistical guarantees but are too inefficient for practical use). According to the sequential inductive model, learning is a sequence of decisions which are informed by training data. By analyzing induction at the level of these decisions, and by utilizing the minimum data necessary to make each decision, sequential inductive techniques can provide the strong statistical guarantees of worst-case methods, but with substantially less data than those methods require. The sequential inductive model is also useful as a method for determining a sufficient sample size for inductive learning and as such, is relevant to megainduction,where the preponderance of data introduces problems of scale. The peepholing and decision-theoretic subsampling approaches of Catlett, Musick, and Russell, which address problems of scale in inductive learning, have informed my work. The technique that I have developed generalizes and extends these prior approaches to megainduction. _______________________ This paper has not already been accepted by and is not currently under review for a journal or other conference. Nor will it be submitted for such during IJCAI’s review period. This work is supported by the National Science Foundation under Grant NSF–IRI–92-09394.