1215 search results - page 11 / 243 » Dimensions of machine learning in design |
COMBINATORICA
2010
14 years 6 months ago
2010
We show that the topological cycle space of a locally finite graph is a canonical quotient of the first singular homology group of its Freudenthal compactification, and we charact...
114
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JCSS
2008
14 years 11 months ago
2008
We use techniques from sample-complexity in machine learning to reduce problems of incentive-compatible mechanism design to standard algorithmic questions, for a broad class of re...
COLT
2001
Springer
15 years 4 months ago
2001
Springer
The notion of embedding a class of dichotomies in a class of linear half spaces is central to the support vector machines paradigm. We examine the question of determining the mini...
COLT
2008
Springer
15 years 1 months ago
2008
Springer
A kernel over the Boolean domain is said to be reflection-invariant, if its value does not change when we flip the same bit in both arguments. (Many popular kernels have this prop...
132
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COLT
1993
Springer
15 years 3 months ago
1993
Springer
The Vapnik-Chervonenkis (V-C) dimension is an important combinatorial tool in the analysis of learning problems in the PAC framework. For polynomial learnability, we seek upper bou...