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» Effect of Batch Learning in Multilayer Neural Networks
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CONNECTION
2004
98views more  CONNECTION 2004»
13 years 7 months ago
Self-refreshing memory in artificial neural networks: learning temporal sequences without catastrophic forgetting
While humans forget gradually, highly distributed connectionist networks forget catastrophically: newly learned information often completely erases previously learned information. ...
Bernard Ans, Stephane Rousset, Robert M. French, S...
NPL
2000
99views more  NPL 2000»
13 years 7 months ago
On the Internal Representations of Product Units
This paper explores internal representation power of product units [1] that act as the functional nodes in the hidden layer of a multi-layer feedforward network. Interesting proper...
Jung-Hua Wang, Yi-Wei Yu, Jia-Horng Tsai
ICMLA
2003
13 years 9 months ago
The Consolidation of Neural Network Task Knowledge
— Fundamental to the problem of lifelong machine learning is how to consolidate the knowledge of a learned task within a long-term memory structure (domain knowledge) without the...
Daniel L. Silver, Peter McCracken
GECCO
2009
Springer
135views Optimization» more  GECCO 2009»
14 years 2 months ago
Neuroevolutionary reinforcement learning for generalized helicopter control
Helicopter hovering is an important challenge problem in the field of reinforcement learning. This paper considers several neuroevolutionary approaches to discovering robust cont...
Rogier Koppejan, Shimon Whiteson
ICANN
2005
Springer
14 years 29 days ago
Reducing the Effect of Out-Voting Problem in Ensemble Based Incremental Support Vector Machines
Although Support Vector Machines (SVMs) have been successfully applied to solve a large number of classification and regression problems, they suffer from the catastrophic forgetti...
Zeki Erdem, Robi Polikar, Fikret S. Gürgen, N...