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JMLR
2012
13 years 3 days ago
Krylov Subspace Descent for Deep Learning
In this paper, we propose a second order optimization method to learn models where both the dimensionality of the parameter space and the number of training samples is high. In ou...
Oriol Vinyals, Daniel Povey
AAAI
2012
13 years 2 days ago
Kernel-Based Reinforcement Learning on Representative States
Markov decision processes (MDPs) are an established framework for solving sequential decision-making problems under uncertainty. In this work, we propose a new method for batchmod...
Branislav Kveton, Georgios Theocharous
JMLR
2012
13 years 3 days ago
Marginal Regression For Multitask Learning
Variable selection is an important and practical problem that arises in analysis of many high-dimensional datasets. Convex optimization procedures that arise from relaxing the NP-...
Mladen Kolar, Han Liu
55
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TSMC
2002
69views more  TSMC 2002»
14 years 9 months ago
A new learning algorithm for the hierarchical structure learning automata operating in the nonstationary S-model random environm
An extended algorithm of the relative reward strength algorithm is proposed. It is shown that the proposed algorithm ensures the convergence with probability 1 to the optimal path ...
Norio Baba, Yoshio Mogami
COLT
1998
Springer
15 years 1 months ago
Learning One-Variable Pattern Languages in Linear Average Time
A new algorithm for learning one-variable pattern languages is proposed and analyzed with respect to its average-case behavior. We consider the total learning time that takes into...
Rüdiger Reischuk, Thomas Zeugmann