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» Learning for Dynamic Subsumption
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ICML
2007
IEEE
16 years 2 months ago
Tracking value function dynamics to improve reinforcement learning with piecewise linear function approximation
Reinforcement learning algorithms can become unstable when combined with linear function approximation. Algorithms that minimize the mean-square Bellman error are guaranteed to co...
Chee Wee Phua, Robert Fitch
ICCV
2003
IEEE
15 years 7 months ago
Active Concept Learning for Image Retrieval in Dynamic Databases
Concept learning in content-based image retrieval (CBIR) systems is a challenging task. This paper presents an active concept learning approach based on mixture model to deal with...
Anlei Dong, Bir Bhanu
ROBOCUP
2005
Springer
134views Robotics» more  ROBOCUP 2005»
15 years 7 months ago
Simultaneous Learning to Acquire Competitive Behaviors in Multi-agent System Based on Modular Learning System
The existing reinforcement learning approaches have been suffering from the policy alternation of others in multiagent dynamic environments. A typical example is a case of RoboCup...
Yasutake Takahashi, Kazuhiro Edazawa, Kentarou Nom...
CEC
2009
IEEE
15 years 8 months ago
Hyper-learning for population-based incremental learning in dynamic environments
— The population-based incremental learning (PBIL) algorithm is a combination of evolutionary optimization and competitive learning. Recently, the PBIL algorithm has been applied...
Shengxiang Yang, Hendrik Richter
CVIU
2004
115views more  CVIU 2004»
15 years 1 months ago
Dynamic learning from multiple examples for semantic object segmentation and search
We present a novel ``dynamic learning'' approach for an intelligent image database system to automatically improve object segmentation and labeling without user interven...
Yaowu Xu, Eli Saber, A. Murat Tekalp