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» Learning with the Set Covering Machine
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ICML
2005
IEEE
14 years 9 months ago
Why skewing works: learning difficult Boolean functions with greedy tree learners
We analyze skewing, an approach that has been empirically observed to enable greedy decision tree learners to learn "difficult" Boolean functions, such as parity, in the...
Bernard Rosell, Lisa Hellerstein, Soumya Ray, Davi...
MLG
2007
Springer
14 years 3 months ago
Transductive Rademacher Complexities for Learning Over a Graph
Recent investigations [12, 2, 8, 5, 6] and [11, 9] indicate the use of a probabilistic (’learning’) perspective of tasks defined on a single graph, as opposed to the traditio...
Kristiaan Pelckmans, Johan A. K. Suykens
ICML
2004
IEEE
14 years 9 months ago
Communication complexity as a lower bound for learning in games
A fast-growing body of research in the AI and machine learning communities addresses learning in games, where there are multiple learners with different interests. This research a...
Vincent Conitzer, Tuomas Sandholm
COLT
2008
Springer
13 years 10 months ago
How Local Should a Learning Method Be?
We consider the question of why modern machine learning methods like support vector machines outperform earlier nonparametric techniques like kNN. Our approach investigates the lo...
Alon Zakai, Yaacov Ritov
ICML
2006
IEEE
14 years 9 months ago
Agnostic active learning
We state and analyze the first active learning algorithm which works in the presence of arbitrary forms of noise. The algorithm, A2 (for Agnostic Active), relies only upon the ass...
Maria-Florina Balcan, Alina Beygelzimer, John Lang...