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» Learning Halfspaces with Malicious Noise
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FOCS
1999
IEEE
15 years 1 months ago
An Algorithmic Theory of Learning: Robust Concepts and Random Projection
We study the phenomenon of cognitive learning from an algorithmic standpoint. How does the brain effectively learn concepts from a small number of examples despite the fact that e...
Rosa I. Arriaga, Santosh Vempala
93
Voted
FOCS
2008
IEEE
15 years 4 months ago
Learning Geometric Concepts via Gaussian Surface Area
We study the learnability of sets in Rn under the Gaussian distribution, taking Gaussian surface area as the “complexity measure” of the sets being learned. Let CS denote the ...
Adam R. Klivans, Ryan O'Donnell, Rocco A. Servedio
COLT
2008
Springer
14 years 11 months ago
Learning in the Limit with Adversarial Disturbances
We study distribution-dependent, data-dependent, learning in the limit with adversarial disturbance. We consider an optimization-based approach to learning binary classifiers from...
Constantine Caramanis, Shie Mannor
ICPPW
2006
IEEE
15 years 3 months ago
m-LPN: An Approach Towards a Dependable Trust Model for Pervasive Computing Applications
Trust, the fundamental basis of ‘cooperation’ – one of the most important characteristics for the performance of pervasive ad hoc network-- is under serious threat with the ...
Munirul M. Haque, Sheikh Iqbal Ahamed
CEAS
2008
Springer
14 years 11 months ago
Filtering Email Spam in the Presence of Noisy User Feedback
Recent email spam filtering evaluations, such as those conducted at TREC, have shown that near-perfect filtering results are attained with a variety of machine learning methods wh...
D. Sculley, Gordon V. Cormack