By Manfred Warmuth (auth.), Shai Ben-David (eds.)
ISBN-10: 3540626859
ISBN-13: 9783540626855
This e-book constitutes the refereed court cases of the 3rd eu convention on Computational studying concept, EuroCOLT'97, held in Jerusalem, Israel, in March 1997.
The booklet offers 25 revised complete papers rigorously chosen from a complete of 36 top of the range submissions. the quantity spans the entire spectrum of computational studying concept, with a undeniable emphasis on mathematical types of desktop studying. one of the themes addressed are laptop studying, neural nets, information, inductive inference, computational complexity, details thought, and theoretical physics.
Read or Download Computational Learning Theory: Third European Conference, EuroCOLT '97 Jerusalem, Israel, March 17–19, 1997 Proceedings PDF
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Extra resources for Computational Learning Theory: Third European Conference, EuroCOLT '97 Jerusalem, Israel, March 17–19, 1997 Proceedings
Example text
If the graph is a split graph, then we obtain some candidate(s) for being a minterm. Regardless of whether that graph was a split graph, it is possible t h a t v contains more than k relevant l's. Then v must not contain any irrelevant l's, since then at least one of its children would be classified 1 (instead of *). Also, v cannot have more than k + 1 relevant l's, because in that case v's children would not be in the boundary region. Thus v must contain precisely k + 1 relevant l's, no irrelevant l's, and all of v's children are minterms.
Bounded degree multivariate polynomials over Z,~ can be interpolated using substitution queries. 29 T h e former follows after we show how to represent multivariate polynomials as m a t r i x functions. T h e latter follows using a generalization of Schwartz's L e m m a [9] after a bound on the number of possible zeros of any target has been established. T h e remainder of the p a p e r is organized as follows. Section 2 contains some relevant definitions required for an understanding of our learning algorithm.
Each edge corresponds to a pair of variables xi and xj. This pair of variables, in turn, corresponds to the grandchild of v obtained by turning off xi and xj. The label of this edge is the answer received from an IBQ on that grandchild. If any such grandchild has IBQ 1, then we resume the reduce process with t h a t grandchild. Thus in what follows we assume t h a t this does not happen. ), instead of considering cliques and independent sets. L e m m a 3. Graph G1 is a split graph. Proof sketch.
Computational Learning Theory: Third European Conference, EuroCOLT '97 Jerusalem, Israel, March 17–19, 1997 Proceedings by Manfred Warmuth (auth.), Shai Ben-David (eds.)
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