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» On the Complexity of Function Learning
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ICML
2007
IEEE
16 years 5 months ago
Sample compression bounds for decision trees
We propose a formulation of the Decision Tree learning algorithm in the Compression settings and derive tight generalization error bounds. In particular, we propose Sample Compres...
Mohak Shah
NPL
2002
151views more  NPL 2002»
15 years 4 months ago
Additive Composition of Supervised Self Organizing Maps
The learning of complex relationships can be decomposed into several neural networks. The modular organization is determined by prior knowledge of the problem that permits to split...
Jean-Luc Buessler, Jean-Philippe Urban, Julien Gre...
ATAL
2008
Springer
15 years 6 months ago
Adaptive Kanerva-based function approximation for multi-agent systems
In this paper, we show how adaptive prototype optimization can be used to improve the performance of function approximation based on Kanerva Coding when solving largescale instanc...
Cheng Wu, Waleed Meleis
BMCBI
2006
92views more  BMCBI 2006»
15 years 4 months ago
Demonstration of two novel methods for predicting functional siRNA efficiency
Background: siRNAs are small RNAs that serve as sequence determinants during the gene silencing process called RNA interference (RNAi). It is well know that siRNA efficiency is cr...
Peilin Jia, Tieliu Shi, Yu-Dong Cai, Yixue Li
SIAMIS
2010
378views more  SIAMIS 2010»
14 years 11 months ago
Global Interactions in Random Field Models: A Potential Function Ensuring Connectedness
Markov random field (MRF) models, including conditional random field models, are popular in computer vision. However, in order to be computationally tractable, they are limited to ...
Sebastian Nowozin, Christoph H. Lampert