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ML
2007
ACM
106views Machine Learning» more  ML 2007»
15 years 1 months ago
Surrogate maximization/minimization algorithms and extensions
Abstract Surrogate maximization (or minimization) (SM) algorithms are a family of algorithms that can be regarded as a generalization of expectation-maximization (EM) algorithms. A...
Zhihua Zhang, James T. Kwok, Dit-Yan Yeung
ML
2007
ACM
108views Machine Learning» more  ML 2007»
15 years 1 months ago
Unconditional lower bounds for learning intersections of halfspaces
We prove new lower bounds for learning intersections of halfspaces, one of the most important concept classes in computational learning theory. Our main result is that any statist...
Adam R. Klivans, Alexander A. Sherstov
ICML
2009
IEEE
16 years 2 months ago
Regularization and feature selection in least-squares temporal difference learning
We consider the task of reinforcement learning with linear value function approximation. Temporal difference algorithms, and in particular the Least-Squares Temporal Difference (L...
J. Zico Kolter, Andrew Y. Ng
ICML
2005
IEEE
16 years 2 months ago
Active learning for sampling in time-series experiments with application to gene expression analysis
Many time-series experiments seek to estimate some signal as a continuous function of time. In this paper, we address the sampling problem for such experiments: determining which ...
Rohit Singh, Nathan Palmer, David K. Gifford, Bonn...
AIEDAM
1998
87views more  AIEDAM 1998»
15 years 1 months ago
Learning to set up numerical optimizations of engineering designs
Gradient-based numerical optimization of complex engineering designs offers the promise of rapidly producing better designs. However, such methods generally assume that the object...
Mark Schwabacher, Thomas Ellman, Haym Hirsh