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» A Boosting Algorithm for Regression
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ESANN
2007
15 years 5 months ago
Model Selection for Kernel Probit Regression
Abstract. The convex optimisation problem involved in fitting a kernel probit regression (KPR) model can be solved efficiently via an iteratively re-weighted least-squares (IRWLS)...
Gavin C. Cawley
CSDA
2006
71views more  CSDA 2006»
15 years 4 months ago
Computation of Huber's M-estimates for a block-angular regression problem
Huber's M-estimation technique is applied to a block-angular regression problem, which may arise from some applications. A recursive, modified Newton approach to computing th...
Xiao-Wen Chang
ML
2002
ACM
129views Machine Learning» more  ML 2002»
15 years 3 months ago
Model Selection for Small Sample Regression
Model selection is an important ingredient of many machine learning algorithms, in particular when the sample size in small, in order to strike the right trade-off between overfitt...
Olivier Chapelle, Vladimir Vapnik, Yoshua Bengio
KDD
2009
ACM
215views Data Mining» more  KDD 2009»
16 years 4 months ago
Large-scale sparse logistic regression
Logistic Regression is a well-known classification method that has been used widely in many applications of data mining, machine learning, computer vision, and bioinformatics. Spa...
Jun Liu, Jianhui Chen, Jieping Ye
155
Voted
WEBDB
2010
Springer
155views Database» more  WEBDB 2010»
15 years 9 months ago
Learning Topical Transition Probabilities in Click Through Data with Regression Models
The transition of search engine users’ intents has been studied for a long time. The knowledge of intent transition, once discovered, can yield a better understanding of how di...
Xiao Zhang, Prasenjit Mitra