Abstract
Abstract Although many algorithms have been presented for empirical inductive learning, there are few algorithms whose sample complexity and computational complexity have been theoretically analysed. Moreover, although many algorithms have been presented in computational learning theory, few algorithms can be applied to empirical inductive learning problems. Our final target is to present efficient algorithms for attribute-based inductive learning, whose sample complexity and computational complexity lend themselves to theoretical analysis. This paper presents efficient algorithms for attribute-based inductive learning.
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