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ICML
2005
IEEE
14 years 8 months ago
Supervised versus multiple instance learning: an empirical comparison
We empirically study the relationship between supervised and multiple instance (MI) learning. Algorithms to learn various concepts have been adapted to the MI representation. Howe...
Soumya Ray, Mark Craven
KDD
2007
ACM
132views Data Mining» more  KDD 2007»
14 years 8 months ago
A scalable modular convex solver for regularized risk minimization
A wide variety of machine learning problems can be described as minimizing a regularized risk functional, with different algorithms using different notions of risk and different r...
Choon Hui Teo, Alex J. Smola, S. V. N. Vishwanatha...
ECML
2006
Springer
13 years 11 months ago
Approximate Policy Iteration for Closed-Loop Learning of Visual Tasks
Abstract. Approximate Policy Iteration (API) is a reinforcement learning paradigm that is able to solve high-dimensional, continuous control problems. We propose to exploit API for...
Sébastien Jodogne, Cyril Briquet, Justus H....
GECCO
2010
Springer
184views Optimization» more  GECCO 2010»
13 years 11 months ago
A mono surrogate for multiobjective optimization
Most surrogate approaches to multi-objective optimization build a surrogate model for each objective. These surrogates can be used inside a classical Evolutionary Multiobjective O...
Ilya Loshchilov, Marc Schoenauer, Michèle S...
NIPS
2001
13 years 9 months ago
Online Learning with Kernels
Abstract--Kernel-based algorithms such as support vector machines have achieved considerable success in various problems in batch setting, where all of the training data is availab...
Jyrki Kivinen, Alex J. Smola, Robert C. Williamson