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JMLR
2008
131views more  JMLR 2008»
14 years 9 months ago
On Relevant Dimensions in Kernel Feature Spaces
We show that the relevant information of a supervised learning problem is contained up to negligible error in a finite number of leading kernel PCA components if the kernel matche...
Mikio L. Braun, Joachim M. Buhmann, Klaus-Robert M...
ML
2006
ACM
110views Machine Learning» more  ML 2006»
14 years 9 months ago
Classification-based objective functions
Backpropagation, similar to most learning algorithms that can form complex decision surfaces, is prone to overfitting. This work presents classification-based objective functions, ...
Michael Rimer, Tony Martinez
ICML
2004
IEEE
15 years 10 months ago
Learning with non-positive kernels
In this paper we show that many kernel methods can be adapted to deal with indefinite kernels, that is, kernels which are not positive semidefinite. They do not satisfy Mercer...
Alexander J. Smola, Cheng Soon Ong, Stéphan...
ICML
2002
IEEE
15 years 10 months ago
Reinforcement Learning and Shaping: Encouraging Intended Behaviors
We explore dynamic shaping to integrate our prior beliefs of the final policy into a conventional reinforcement learning system. Shaping provides a positive or negative artificial...
Adam Laud, Gerald DeJong
ICML
1998
IEEE
15 years 10 months ago
Learning Collaborative Information Filters
Predicting items a user would like on the basis of other users' ratings for these items has become a well-established strategy adopted by many recommendation services on the ...
Daniel Billsus, Michael J. Pazzani