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» A comparison of empirical and model-driven optimization
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ATAL
2009
Springer
15 years 4 months ago
Generalized model learning for reinforcement learning in factored domains
Improving the sample efficiency of reinforcement learning algorithms to scale up to larger and more realistic domains is a current research challenge in machine learning. Model-ba...
Todd Hester, Peter Stone
GECCO
2008
Springer
206views Optimization» more  GECCO 2008»
14 years 10 months ago
Improving accuracy of immune-inspired malware detectors by using intelligent features
In this paper, we show that a Bio-inspired classifier’s accuracy can be dramatically improved if it operates on intelligent features. We propose a novel set of intelligent feat...
M. Zubair Shafiq, Syed Ali Khayam, Muddassar Faroo...
MANSCI
2007
90views more  MANSCI 2007»
14 years 9 months ago
Selecting a Selection Procedure
Selection procedures are used in a variety of applications to select the best of a finite set of alternatives. ‘Best’ is defined with respect to the largest mean, but the me...
Jürgen Branke, Stephen E. Chick, Christian Sc...
CVPR
2008
IEEE
15 years 11 months ago
In defense of Nearest-Neighbor based image classification
State-of-the-art image classification methods require an intensive learning/training stage (using SVM, Boosting, etc.) In contrast, non-parametric Nearest-Neighbor (NN) based imag...
Oren Boiman, Eli Shechtman, Michal Irani
NIPS
2008
14 years 11 months ago
Semi-supervised Learning with Weakly-Related Unlabeled Data: Towards Better Text Categorization
The cluster assumption is exploited by most semi-supervised learning (SSL) methods. However, if the unlabeled data is merely weakly related to the target classes, it becomes quest...
Liu Yang, Rong Jin, Rahul Sukthankar