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131
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NIPS
2008
15 years 5 months ago
An interior-point stochastic approximation method and an L1-regularized delta rule
The stochastic approximation method is behind the solution to many important, actively-studied problems in machine learning. Despite its farreaching application, there is almost n...
Peter Carbonetto, Mark Schmidt, Nando de Freitas
98
Voted
NIPS
2007
15 years 5 months ago
On higher-order perceptron algorithms
A new algorithm for on-line learning linear-threshold functions is proposed which efficiently combines second-order statistics about the data with the ”logarithmic behavior” ...
Claudio Gentile, Fabio Vitale, Cristian Brotto
105
Voted
AIA
2006
15 years 5 months ago
Some Experiments Around a Neural Network for Multimodal Associations
This paper presents a study of the model of triple BAM by [11] which is an improved variation of the original BAM model by [7]. This class of model aims at integrating different s...
Yann Boniface, Reghis Abdelmalek
128
Voted
ICONIP
2010
15 years 2 months ago
Improving Recurrent Neural Network Performance Using Transfer Entropy
Abstract. Reservoir computing approaches have been successfully applied to a variety of tasks. An inherent problem of these approaches, is, however, their variation in performance ...
Oliver Obst, Joschka Boedecker, Minoru Asada
138
Voted
COLT
2010
Springer
15 years 1 months ago
Hedging Structured Concepts
We develop an online algorithm called Component Hedge for learning structured concept classes when the loss of a structured concept sums over its components. Example classes inclu...
Wouter M. Koolen, Manfred K. Warmuth, Jyrki Kivine...