| Dana Angluin. Negative Results for Equivalence Queries Machine Learning, 5:121-150, 1990. |
....p 3 oracle. The hypothesis class for the above learning algorithms is the class of circuits of larger but polynomially related size. Also, the algorithms can be adapted to learn the class of DNF formulas with hypothesis class consisting of depth 3 formulas (by the work of Angluin [A90], this is optimal in the sense that the hypothesis class cannot be reduced to depth 2 DNF formulas) We also investigate the power of an NP oracle in the context of learning with membership queries. We show that there are deterministic learning algorithms that use membership queries and an ....
....arbitrary circuits. The hypothesis class for the above learning algorithms is the class of circuits of larger but polynomially related size. Also, the algorithms can be adapted to learn the class of DNF formulas with hypothesis class consisting of depth 3 formulas (by the work of Angluin [A90], this is optimal in the sense that learning DNF is impossible when the hypothesis class is depth 2 DNF formulas, regardless of the computational power of the learner) Learning models where the learner has an NP oracle (or Sigma p 3 oracle) are admittedly quite generous, so some motivation ....
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Dana Angluin. Negative Results for Equivalence Queries Machine Learning, 5:121-150, 1990.
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Dana Angluin. Negative Results for Equivalence Queries Machine Learning, 5:121-150, 1990.
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