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» Approximation Methods for Supervised Learning
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IJCAI
2003
13 years 11 months ago
Multiple-Goal Reinforcement Learning with Modular Sarsa(0)
We present a new algorithm, GM-Sarsa(0), for finding approximate solutions to multiple-goal reinforcement learning problems that are modeled as composite Markov decision processe...
Nathan Sprague, Dana H. Ballard
JMLR
2010
125views more  JMLR 2010»
13 years 4 months ago
Continuous Time Bayesian Network Reasoning and Learning Engine
We present a continuous time Bayesian network reasoning and learning engine (CTBN-RLE). A continuous time Bayesian network (CTBN) provides a compact (factored) description of a co...
Christian R. Shelton, Yu Fan, William Lam, Joon Le...
ICPR
2008
IEEE
14 years 4 months ago
Fast multiple instance learning via L1, 2 logistic regression
In this paper, we develop an efficient logistic regression model for multiple instance learning that combines L1 and L2 regularisation techniques. An L1 regularised logistic regr...
Zhouyu Fu, Antonio Robles-Kelly
ICPR
2006
IEEE
14 years 4 months ago
Regularized Locality Preserving Learning of Pre-Image Problem in Kernel Principal Component Analysis
In this paper, we address the pre-image problem in kernel principal component analysis (KPCA). The preimage problem finds a pattern as the pre-image of a feature vector defined in...
Weishi Zheng, Jian-Huang Lai
IJIT
2004
13 years 11 months ago
Learning of Class Membership Values by Ellipsoidal Decision Regions
A novel method of learning complex fuzzy decision regions in the n-dimensional feature space is proposed. Through the fuzzy decision regions, a given pattern's class membershi...
Leehter Yao, Chin-chin Lin