Inductive Learning from Good Examples
نویسنده
چکیده
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 deenitions 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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تاریخ انتشار 1991