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» Adapting RBF Neural Networks to Multi-Instance Learning
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ICML
1995
IEEE
14 years 8 months ago
Visualizing High-Dimensional Structure with the Incremental Grid Growing Neural Network
Understanding high-dimensional real world data usually requires learning the structure of the data space. The structure maycontain high-dimensional clusters that are related in co...
Justine Blackmore, Risto Miikkulainen
NN
2008
Springer
153views Neural Networks» more  NN 2008»
13 years 7 months ago
A biologically motivated visual memory architecture for online learning of objects
We present a biologically motivated architecture for object recognition that is based on a hierarchical feature-detection model in combination with a memory architecture that impl...
Stephan Kirstein, Heiko Wersing, Edgar Körner
IJCNN
2006
IEEE
14 years 1 months ago
An Adaptive Penalty-Based Learning Extension for Backpropagation and its Variants
Abstract— Over the years, many improvements and refinements of the backpropagation learning algorithm have been reported. In this paper, a new adaptive penalty-based learning ex...
Boris Jansen, Kenji Nakayama
IJCNN
2008
IEEE
14 years 2 months ago
A comparison of fuzzy ARTMAP and Gaussian ARTMAP neural networks for incremental learning
Abstract— Automatic pattern classifiers that allow for incremental learning can adapt internal class models efficiently in response to new information, without having to retrai...
Eric Granger, Jean-François Connolly, Rober...
ICANN
2001
Springer
14 years 4 days ago
Nonlinear Adaptive Beliefs and the Dynamics of Financial Markets: The Role of the Evolutionary Fitness Measure
We introduce a simple asset pricing model with two types of adaptively learning traders, fundamentalists and technical traders. Traders update their beliefs according to past perfo...
Andrea Gaunersdorfer, Cars H. Hommes