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GECCO
2006
Springer
159views Optimization» more  GECCO 2006»
13 years 11 months ago
Standard and averaging reinforcement learning in XCS
This paper investigates reinforcement learning (RL) in XCS. First, it formally shows that XCS implements a method of generalized RL based on linear approximators, in which the usu...
Pier Luca Lanzi, Daniele Loiacono
TKDE
2008
195views more  TKDE 2008»
13 years 7 months ago
Learning a Maximum Margin Subspace for Image Retrieval
One of the fundamental problems in Content-Based Image Retrieval (CBIR) has been the gap between low-level visual features and high-level semantic concepts. To narrow down this gap...
Xiaofei He, Deng Cai, Jiawei Han
AAAI
1998
13 years 9 months ago
Boosting in the Limit: Maximizing the Margin of Learned Ensembles
The "minimum margin" of an ensemble classifier on a given training set is, roughly speaking, the smallest vote it gives to any correct training label. Recent work has sh...
Adam J. Grove, Dale Schuurmans
NIPS
2000
13 years 9 months ago
Machine Learning for Video-Based Rendering
We present techniques for rendering and animation of realistic scenes by analyzing and training on short video sequences. This work extends the new paradigm for computer animation...
Arno Schödl, Irfan A. Essa
CVPR
1999
IEEE
14 years 9 months ago
Time-Series Classification Using Mixed-State Dynamic Bayesian Networks
We present a novel mixed-state dynamic Bayesian network (DBN) framework for modeling and classifying timeseries data such as object trajectories. A hidden Markov model (HMM) of di...
Vladimir Pavlovic, Brendan J. Frey, Thomas S. Huan...