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IJAR
2008
119views more  IJAR 2008»
13 years 7 months ago
Adapting Bayes network structures to non-stationary domains
When an incremental structural learning method gradually modifies a Bayesian network (BN) structure to fit observations, as they are read from a database, we call the process stru...
Søren Holbech Nielsen, Thomas D. Nielsen
GECCO
2008
Springer
177views Optimization» more  GECCO 2008»
13 years 8 months ago
Reduced computation for evolutionary optimization in noisy environment
Evolutionary Algorithms’ (EAs’) application to real world optimization problems often involves expensive fitness function evaluation. Naturally this has a crippling effect on ...
Maumita Bhattacharya
NEUROSCIENCE
2001
Springer
14 years 1 days ago
Analysis and Synthesis of Agents That Learn from Distributed Dynamic Data Sources
We propose a theoretical framework for specification and analysis of a class of learning problems that arise in open-ended environments that contain multiple, distributed, dynamic...
Doina Caragea, Adrian Silvescu, Vasant Honavar
ECAL
2007
Springer
14 years 1 months ago
Neuroevolution of Agents Capable of Reactive and Deliberative Behaviours in Novel and Dynamic Environments
Both reactive and deliberative qualities are essential for a good action selection mechanism. We present a model that embodies a hybrid of two very different neural network archit...
Edward Robinson, Timothy Ellis, Alastair Channon
PREMI
2005
Springer
14 years 1 months ago
Hybrid Hierarchical Learning from Dynamic Scenes
The work proposes a hierarchical architecture for learning amic scenes at various levels of knowledge abstraction. The raw visual information is processed at different stages to g...
Prithwijit Guha, Pradeep Vaghela, Pabitra Mitra, K...