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FOCS
1990
IEEE
14 years 24 days ago
Separating Distribution-Free and Mistake-Bound Learning Models over the Boolean Domain
Two of the most commonly used models in computational learning theory are the distribution-free model in which examples are chosen from a fixed but arbitrary distribution, and the ...
Avrim Blum
GECCO
2006
Springer
162views Optimization» more  GECCO 2006»
14 years 12 days ago
Evolutionary learning with kernels: a generic solution for large margin problems
In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and s...
Ingo Mierswa
KER
2007
90views more  KER 2007»
13 years 8 months ago
PLTOOL: A knowledge engineering tool for planning and learning
AI planning solves the problem of generating a correct and efficient ordered set of instantiated activities, from a knowledge base of generic actions, which when executed will tra...
Susana Fernández, Daniel Borrajo, Raquel Fu...
KBSE
2005
IEEE
14 years 2 months ago
Automated test generation for engineering applications
In test generation based on model-checking, white-box test criteria are represented as trap conditions written in a temporal logic. A model checker is used to refute trap conditio...
Songtao Xia, Ben Di Vito, César Muño...
PLDI
2009
ACM
14 years 3 months ago
Snugglebug: a powerful approach to weakest preconditions
Symbolic analysis shows promise as a foundation for bug-finding, specification inference, verification, and test generation. This paper addresses demand-driven symbolic analysi...
Satish Chandra, Stephen J. Fink, Manu Sridharan