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» Approximate Learning of Dynamic Models
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ML
2002
ACM
146views Machine Learning» more  ML 2002»
15 years 6 months ago
Kernel Matching Pursuit
Matching Pursuit algorithms learn a function that is a weighted sum of basis functions, by sequentially appending functions to an initially empty basis, to approximate a target fu...
Pascal Vincent, Yoshua Bengio
NN
2006
Springer
140views Neural Networks» more  NN 2006»
15 years 6 months ago
Neural mechanism for stochastic behaviour during a competitive game
Previous studies have shown that non-human primates can generate highly stochastic choice behaviour, especially when this is required during a competitive interaction with another...
Alireza Soltani, Daeyeol Lee, Xiao-Jing Wang
ICML
2005
IEEE
16 years 7 months ago
Combining model-based and instance-based learning for first order regression
T ORDER REGRESSION (EXTENDED ABSTRACT) Kurt Driessensa Saso Dzeroskib a Department of Computer Science, University of Waikato, Hamilton, New Zealand (kurtd@waikato.ac.nz) b Departm...
Kurt Driessens, Saso Dzeroski
NIPS
2001
15 years 7 months ago
Global Coordination of Local Linear Models
High dimensional data that lies on or near a low dimensional manifold can be described by a collection of local linear models. Such a description, however, does not provide a glob...
Sam T. Roweis, Lawrence K. Saul, Geoffrey E. Hinto...
ATAL
2005
Springer
15 years 12 months ago
Modeling opponent decision in repeated one-shot negotiations
In many negotiation and bargaining scenarios, a particular agent may need to interact repeatedly with another agent. Typically, these interactions take place under incomplete info...
Sabyasachi Saha, Anish Biswas, Sandip Sen