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NN
2010
Springer
225views Neural Networks» more  NN 2010»
13 years 6 months ago
Learning to imitate stochastic time series in a compositional way by chaos
This study shows that a mixture of RNN experts model can acquire the ability to generate sequences that are combination of multiple primitive patterns by means of self-organizing ...
Jun Namikawa, Jun Tani
ICANN
2009
Springer
14 years 2 months ago
Evolving Memory Cell Structures for Sequence Learning
The best recent supervised sequence learning methods use gradient descent to train networks of miniature nets called memory cells. The most popular cell structure seems somewhat ar...
Justin Bayer, Daan Wierstra, Julian Togelius, J&uu...
ISNN
2004
Springer
14 years 1 months ago
Unsupervised Learning for Hierarchical Clustering Using Statistical Information
This paper proposes a novel hierarchical clustering method that can classify given data without specified knowledge of the number of classes. In this method, at each node of a hie...
Masaru Okamoto, Nan Bu, Toshio Tsuji
ICANN
2007
Springer
14 years 2 months ago
Unbiased SVM Density Estimation with Application to Graphical Pattern Recognition
Abstract. Classification of structured data (i.e., data that are represented as graphs) is a topic of interest in the machine learning community. This paper presents a different,...
Edmondo Trentin, Ernesto Di Iorio
ICANN
2005
Springer
14 years 1 months ago
Learning Features of Intermediate Complexity for the Recognition of Biological Motion
Humans can recognize biological motion from strongly impoverished stimuli, like point-light displays. Although the neural mechanism underlying this robust perceptual process have n...
Rodrigo Sigala, Thomas Serre, Tomaso Poggio, Marti...