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» Approximation Methods for Supervised Learning
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ICML
2002
IEEE
16 years 3 months ago
Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs
One of the central challenges in reinforcement learning is to balance the exploration/exploitation tradeoff while scaling up to large problems. Although model-based reinforcement ...
Carlos Guestrin, Relu Patrascu, Dale Schuurmans
ISMB
2004
15 years 3 months ago
Predicting gene regulation by sigma factors in Bacillus subtilis from genome-wide data
Motivation: Sigma factors regulate the expression of genes in Bacillus subtilis at the transcriptional level. First we assess the ability of currently available gene regulatory ne...
Michiel J. L. de Hoon, Yuko Makita, Seiya Imoto, K...
IJON
2007
184views more  IJON 2007»
15 years 2 months ago
Convex incremental extreme learning machine
Unlike the conventional neural network theories and implementations, Huang et al. [Universal approximation using incremental constructive feedforward networks with random hidden n...
Guang-Bin Huang, Lei Chen
NIPS
1996
15 years 3 months ago
Exploiting Model Uncertainty Estimates for Safe Dynamic Control Learning
Model learning combined with dynamic programming has been shown to be e ective for learning control of continuous state dynamic systems. The simplest method assumes the learned mod...
Jeff G. Schneider
114
Voted
NIPS
2007
15 years 3 months ago
Transfer Learning using Kolmogorov Complexity: Basic Theory and Empirical Evaluations
In transfer learning we aim to solve new problems using fewer examples using information gained from solving related problems. Transfer learning has been successful in practice, a...
M. M. Mahmud, Sylvian R. Ray