Towards a Practical Estimate of Training Sample Size
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1993-11-01T00:00:00Z
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Abstract
The purpose of this paper is to introduce a simple learning model that allows one to draw conclusions about the number of distinct
training examples required to learn some boolean function with at least accuracy ${\alpha}$ and probability ${\delta}$ across a general class
of learning algorithms. The motivation for this work stems from the inability of learning theoretical models to suggest reasonable sample bounds. Reducing sample size is essential in the wake of expected costs for labeling patterns by a teacher (e.g. human expert). The derived results are then extended from learning functions to learning concepts to make the analysis more realistic. The importance of domain specific knowledge in learning concepts is
discussed and incorporated into the model in form of identifying impossible training patterns. Several possible sources for these
impossibilities are pointed out. The paper is concluded with 2 representative examples.