83 search results - page 12 / 17 » Neural Network Learning: Testing Bounds on Sample Complexity |
BMCBI
2007
13 years 7 months ago
2007
Background: Causal networks based on the vector autoregressive (VAR) process are a promising statistical tool for modeling regulatory interactions in a cell. However, learning the...
NN
2010
Springer
13 years 5 months ago
2010
Springer
We present a model-free reinforcement learning method for partially observable Markov decision problems. Our method estimates a likelihood gradient by sampling directly in paramet...
IJCNN
2007
IEEE
14 years 1 months ago
2007
IEEE
—The 0/1 loss is an important cost function for perceptrons. Nevertheless it cannot be easily minimized by most existing perceptron learning algorithms. In this paper, we propose...
ICANNGA
2009
Springer
14 years 2 months ago
2009
Springer
Many complex, real world phenomena are difficult to study directly using controlled experiments. Instead, the use of computer simulations has become commonplace as a feasible alte...
IJCNN
2006
IEEE
14 years 1 months ago
2006
IEEE
With very noisy data, having plentiful samples eliminates overfitting in nonlinear regression, but not in nonlinear principal component analysis (NLPCA). To overcome this problem...