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» A Minimax Method for Learning Functional Networks
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STOC
2006
ACM
122views Algorithms» more  STOC 2006»
14 years 7 months ago
Fast convergence to Wardrop equilibria by adaptive sampling methods
We study rerouting policies in a dynamic round-based variant of a well known game theoretic traffic model due to Wardrop. Previous analyses (mostly in the context of selfish routi...
Simon Fischer, Harald Räcke, Berthold Vö...
ICCV
2005
IEEE
14 years 9 months ago
Efficient Learning of Relational Object Class Models
We present an efficient method for learning part-based object class models from unsegmented images represented as sets of salient features. A model includes parts' appearance...
Aharon Bar-Hillel, Tomer Hertz, Daphna Weinshall
NN
2008
Springer
143views Neural Networks» more  NN 2008»
13 years 7 months ago
A batch ensemble approach to active learning with model selection
Optimally designing the location of training input points (active learning) and choosing the best model (model selection) are two important components of supervised learning and h...
Masashi Sugiyama, Neil Rubens
KDD
1998
ACM
112views Data Mining» more  KDD 1998»
13 years 11 months ago
Evaluating Usefulness for Dynamic Classification
This paper develops the concept of usefulness in the context of supervised learning. We argue that usefulness can be used to improve the performance of classification rules (as me...
Gholamreza Nakhaeizadeh, Charles Taylor, Carsten L...
STOC
1993
ACM
141views Algorithms» more  STOC 1993»
13 years 11 months ago
Bounds for the computational power and learning complexity of analog neural nets
Abstract. It is shown that high-order feedforward neural nets of constant depth with piecewisepolynomial activation functions and arbitrary real weights can be simulated for Boolea...
Wolfgang Maass