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FOCS
1990
IEEE
15 years 10 months 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
AFRIGRAPH
2007
ACM
15 years 10 months ago
Generic computation of bulletin boards into geometric kernels
Nowadays, many commercial CAD systems are built on proprietary geometric kernels which provide an API containing a set of high level geometric operations (boolean operations, slot...
Mehdi Baba-ali, David Marcheix, Xavier Skapin, Yve...
AOSD
2009
ACM
15 years 10 months ago
The dataflow pointcut: a formal and practical framework
Some security concerns are sensitive to flow of information in a program execution. The dataflow pointcut has been proposed by Masuhara and Kawauchi in order to easily implement s...
Dima Alhadidi, Amine Boukhtouta, Nadia Belblidia, ...
GECCO
2006
Springer
162views Optimization» more  GECCO 2006»
15 years 10 months 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
ATAL
2008
Springer
15 years 8 months ago
Playing games for security: an efficient exact algorithm for solving Bayesian Stackelberg games
In a class of games known as Stackelberg games, one agent (the leader) must commit to a strategy that can be observed by the other agent (the follower or adversary) before the adv...
Praveen Paruchuri, Jonathan P. Pearce, Janusz Mare...