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FLAIRS
2001
13 years 9 months ago
A Method for Evaluating Elicitation Schemes for Probabilities
We present an objective approach for evaluating probability elicitation methods in probabilistic models. Our method draws on ideas from research on learning Bayesian networks: if ...
Haiqin Wang, Denver Dash, Marek J. Druzdzel
IROS
2006
IEEE
190views Robotics» more  IROS 2006»
14 years 2 months ago
Q-RAN: A Constructive Reinforcement Learning Approach for Robot Behavior Learning
Abstract— This paper presents a learning system that uses Qlearning with a resource allocating network (RAN) for behavior learning in mobile robotics. The RAN is used as a functi...
Jun Li, Achim J. Lilienthal, Tomás Mart&iac...
NIPS
2008
13 years 9 months ago
Partially Observed Maximum Entropy Discrimination Markov Networks
Learning graphical models with hidden variables can offer semantic insights to complex data and lead to salient structured predictors without relying on expensive, sometime unatta...
Jun Zhu, Eric P. Xing, Bo Zhang
ISNN
2005
Springer
14 years 1 months ago
Enhanced Fuzzy Single Layer Perceptron
Abstract. In this paper, a method of improving the learning time and convergence rate is proposed to exploit the advantages of artificial neural networks and fuzzy theory to neuron...
Kwang-Baek Kim, Sungshin Kim, Young Hoon Joo, Am S...
IJCNN
2006
IEEE
14 years 2 months ago
Automated Model Selection (AMS) on Finite Mixtures: A Theoretical Analysis
— From the Bayesian Ying-Yang (BYY) harmony learning theory, a harmony function has been developed for finite mixtures with a novel property that its maximization can make model...
Jinwen Ma