1215 search results - page 11 / 243 » Dimensions of machine learning in design |
COMBINATORICA
2010
13 years 2 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...
JCSS
2008
13 years 7 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
14 years 6 days 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
13 years 9 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...
COLT
1993
Springer
13 years 11 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...