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ICML
1996
IEEE
16 years 4 months ago
Sensitive Discount Optimality: Unifying Discounted and Average Reward Reinforcement Learning
Research in reinforcementlearning (RL)has thus far concentrated on two optimality criteria: the discounted framework, which has been very well-studied, and the averagereward frame...
Sridhar Mahadevan
ECML
2000
Springer
15 years 8 months ago
Learning Context-Free Grammars with a Simplicity Bias
We examine the role of simplicity in directing the induction of context-free grammars from sample sentences. We present a rational reconstruction of Wol 's SNPR { the Gridssys...
Pat Langley, Sean Stromsten
ICML
2007
IEEE
16 years 4 months ago
Local learning projections
This paper presents a Local Learning Projection (LLP) approach for linear dimensionality reduction. We first point out that the well known Principal Component Analysis (PCA) essen...
Bernhard Schölkopf, Kai Yu, Mingrui Wu, Shipe...
COLT
2001
Springer
15 years 8 months ago
Learning Additive Models Online with Fast Evaluating Kernels
Abstract. We develop three new techniques to build on the recent advances in online learning with kernels. First, we show that an exponential speed-up in prediction time per trial ...
Mark Herbster
NECO
2008
112views more  NECO 2008»
15 years 4 months ago
Second-Order SMO Improves SVM Online and Active Learning
Iterative learning algorithms that approximate the solution of support vector machines (SVMs) have two potential advantages. First, they allow for online and active learning. Seco...
Tobias Glasmachers, Christian Igel