Abstract
The basic question of how to optimally make use of a finite number of available samples in designing pattern recognition systems is considered. This has several components: optimal use of the samples for design and testing; and the relationship between the number of measurements and the number of samples for various probability structural constraints. A spectrum of possibilities has been demonstrated, placing several apparently conflicting recent results in perspective.
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