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BMCBI
2006
165views more  BMCBI 2006»
13 years 8 months ago
Improved variance estimation of classification performance via reduction of bias caused by small sample size
Background: Supervised learning for classification of cancer employs a set of design examples to learn how to discriminate between tumors. In practice it is crucial to confirm tha...
Ulrika Wickenberg-Bolin, Hanna Göransson, M&a...
NIPS
2001
13 years 10 months ago
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning
Policy gradient methods for reinforcement learning avoid some of the undesirable properties of the value function approaches, such as policy degradation (Baxter and Bartlett, 2001...
Evan Greensmith, Peter L. Bartlett, Jonathan Baxte...
KDD
1998
ACM
120views Data Mining» more  KDD 1998»
14 years 29 days ago
Large Datasets Lead to Overly Complex Models: An Explanation and a Solution
This paper explores unexpected results that lie at the intersection of two common themes in the KDD community: large datasets and the goal of building compact models. Experiments ...
Tim Oates, David Jensen
ESANN
2003
13 years 10 months ago
Fast approximation of the bootstrap for model selection
The bootstrap resampling method may be efficiently used to estimate the generalization error of a family of nonlinear regression models, as artificial neural networks. The main dif...
Geoffroy Simon, Amaury Lendasse, Vincent Wertz, Mi...
IEICET
2007
94views more  IEICET 2007»
13 years 8 months ago
A New Meta-Criterion for Regularized Subspace Information Criterion
In order to obtain better generalization performance in supervised learning, model parameters should be determined appropriately, i.e., they should be determined so that the gener...
Yasushi Hidaka, Masashi Sugiyama