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» Boosting Kernel Models for Regression
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KDD
2002
ACM
109views Data Mining» more  KDD 2002»
14 years 7 months ago
MARK: a boosting algorithm for heterogeneous kernel models
Kristin P. Bennett, Michinari Momma, Mark J. Embre...
FLAIRS
2004
13 years 8 months ago
Random Subspacing for Regression Ensembles
In this work we present a novel approach to ensemble learning for regression models, by combining the ensemble generation technique of random subspace method with the ensemble int...
Niall Rooney, David W. Patterson, Sarab S. Anand, ...
CSMR
2009
IEEE
13 years 11 months ago
Application of TreeNet in Predicting Object-Oriented Software Maintainability: A Comparative Study
There is an increasing interest in more accurate prediction of software maintainability in order to better manage and control software maintenance. Recently, TreeNet has been prop...
Mahmoud O. Elish, Karim O. Elish
ESANN
2007
13 years 8 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
ICML
2004
IEEE
14 years 8 months ago
Training conditional random fields via gradient tree boosting
Conditional Random Fields (CRFs; Lafferty, McCallum, & Pereira, 2001) provide a flexible and powerful model for learning to assign labels to elements of sequences in such appl...
Thomas G. Dietterich, Adam Ashenfelter, Yaroslav B...