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SDM
2008
SIAM
140views Data Mining» more  SDM 2008»
14 years 12 days ago
Large-Scale Many-Class Learning
In many multiclass learning scenarios, the number of classes is relatively large (thousands,...), or the space and time efficiency of the learning system can be crucial. We invest...
Omid Madani, Michael Connor
GECCO
2008
Springer
118views Optimization» more  GECCO 2008»
14 years 1 days ago
Unsupervised learning of echo state networks: balancing the double pole
A possible alternative to fine topology tuning for Neural Network (NN) optimization is to use Echo State Networks (ESNs), recurrent NNs built upon a large reservoir of sparsely r...
Fei Jiang, Hugues Berry, Marc Schoenauer
COGSCI
2010
85views more  COGSCI 2010»
13 years 11 months ago
The Effects of Feature-Label-Order and Their Implications for Symbolic Learning
Symbols enable people to organize and communicate about the world. However, the ways in which symbolic knowledge is learned and then represented in the mind are poorly understood....
Michael Ramscar, Daniel Yarlett, Melody Dye, Katie...
TSMC
2008
229views more  TSMC 2008»
13 years 11 months ago
A Comprehensive Survey of Multiagent Reinforcement Learning
Multiagent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, and economics. The complexity of many task...
Lucian Busoniu, Robert Babuska, Bart De Schutter
AAAI
2011
12 years 11 months ago
Heterogeneous Transfer Learning with RBMs
A common approach in machine learning is to use a large amount of labeled data to train a model. Usually this model can then only be used to classify data in the same feature spac...
Bin Wei, Christopher Pal