Inductive Learning from Good Examples

We study what kind of data may ease the computational complexity of learning of Horn clause theories (in Gold's paradigm) and Boolean functions (in PAC-learning paradigm). We give several definitions of good data (basic and generative representative sets), and develop data-driven algorithms that learn faster from good examples, and degenerate to learn in the limit from the "worst" possible examples. We show that Horn clause theories, k-term DNF and general DNF Boolean functions are polynomially learnable from generative representative presentations.

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