151 search results - page 6 / 31 » Optimized Approximation Algorithm in Neural Networks Without... |
104
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ATAL
2005
Springer
15 years 3 months ago
2005
Springer
Reinforcement learning problems are commonly tackled with temporal difference methods, which use dynamic programming and statistical sampling to estimate the long-term value of ta...
GECCO
2008
Springer
14 years 10 months ago
2008
Springer
One of the major difficulties when applying Multiobjective Evolutionary Algorithms (MOEA) to real world problems is the large number of objective function evaluations. Approximate...
NIPS
1994
14 years 10 months ago
1994
For many types of machine learning algorithms, one can compute the statistically optimal" way to select training data. In this paper, we review how optimal data selection tec...
103
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NN
2000
Springer
14 years 9 months ago
2000
Springer
Piecewise-linear (PWL) neural networks are widely known for their amenability to digital implementation. This paper presents a new algorithm for learning in PWL networks consistin...
70
Voted
ICANN
2007
Springer
15 years 3 months ago
2007
Springer
Input selection in the nonlinear function approximation is important and difficult problem. Neural networks provide good generalization in many cases, but their interpretability is...