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» Boosting Kernel Models for Regression
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ICML
2007
IEEE
16 years 2 months ago
Discriminative learning for differing training and test distributions
We address classification problems for which the training instances are governed by a distribution that is allowed to differ arbitrarily from the test distribution--problems also ...
Michael Brückner, Steffen Bickel, Tobias Sche...
IJCNN
2007
IEEE
15 years 7 months ago
Agnostic Learning versus Prior Knowledge in the Design of Kernel Machines
Abstract— The optimal model parameters of a kernel machine are typically given by the solution of a convex optimisation problem with a single global optimum. Obtaining the best p...
Gavin C. Cawley, Nicola L. C. Talbot
CIKM
2009
Springer
15 years 8 months ago
Feature selection for ranking using boosted trees
Modern search engines have to be fast to satisfy users, so there are hard back-end latency requirements. The set of features useful for search ranking functions, though, continues...
Feng Pan, Tim Converse, David Ahn, Franco Salvetti...
EOR
2007
165views more  EOR 2007»
15 years 1 months ago
Adaptive credit scoring with kernel learning methods
Credit scoring is a method of modelling potential risk of credit applications. Traditionally, logistic regression, linear regression and discriminant analysis are the most popular...
Yingxu Yang
ICCV
2007
IEEE
16 years 3 months ago
Population Shape Regression From Random Design Data
Regression analysis is a powerful tool for the study of changes in a dependent variable as a function of an independent regressor variable, and in particular it is applicable to t...
Bradley C. Davis, P. Thomas Fletcher, Elizabeth Bu...