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The links between genetic algorithms and population-based Markov Chain Monte Carlo (MCMC) methods are explored. Genetic algorithms (GAs) are well-known for their capability to opt...
Feature selection methods have been successfully applied to text categorization but seldom applied to text clustering due to the unavailability of class label information. In this...
AUC(Area Under the Curve) of ROC(Receiver Operating Characteristics) has been recently used as a measure for ranking performanceof learning algorithms. In this paper, wepresent a ...
Shaping can be an effective method for improving the learning rate in reinforcement systems. Previously, shaping has been heuristically motivated and implemented. We provide a for...
Reinforcement learning has been used for training game playing agents. The value function for a complex game must be approximated with a continuous function because the number of ...
Convex programming involves a convex set F Rn and a convex cost function c : F R. The goal of convex programming is to find a point in F which minimizes c. In online convex prog...
An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, wit...