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NIPS
2007
13 years 9 months ago
A General Boosting Method and its Application to Learning Ranking Functions for Web Search
We present a general boosting method extending functional gradient boosting to optimize complex loss functions that are encountered in many machine learning problems. Our approach...
Zhaohui Zheng, Hongyuan Zha, Tong Zhang, Olivier C...
NN
2006
Springer
114views Neural Networks» more  NN 2006»
13 years 7 months ago
Modular learning models in forecasting natural phenomena
Modular model is a particular type of committee machine and is comprised of a set of specialized (local) models each of which is responsible for a particular region of the input s...
Dimitri P. Solomatine, Michael Baskara L. A. Siek
JMLR
2010
162views more  JMLR 2010»
13 years 2 months ago
A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design
An exceedingly large number of scientific and engineering fields are confronted with the need for computer simulations to study complex, real world phenomena or solve challenging ...
Dirk Gorissen, Ivo Couckuyt, Piet Demeester, Tom D...
ICPR
2004
IEEE
14 years 8 months ago
Statistical Classification of Raw Textile Defects
In this paper, the problem of classification of defects occurring in a textile manufacture is addressed. A new classification scheme is devised in which different features, extrac...
Ivan A. Rossi, Manuele Bicego, Vittorio Murino
ICML
2005
IEEE
14 years 8 months ago
Predicting good probabilities with supervised learning
We examine the relationship between the predictions made by different learning algorithms and true posterior probabilities. We show that maximum margin methods such as boosted tre...
Alexandru Niculescu-Mizil, Rich Caruana