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ICML
2007
IEEE
16 years 21 days ago
Supervised feature selection via dependence estimation
We introduce a framework for filtering features that employs the Hilbert-Schmidt Independence Criterion (HSIC) as a measure of dependence between the features and the labels. The ...
Le Song, Alex J. Smola, Arthur Gretton, Karsten M....
ICML
2005
IEEE
16 years 21 days ago
Predictive low-rank decomposition for kernel methods
Low-rank matrix decompositions are essential tools in the application of kernel methods to large-scale learning problems. These decompositions have generally been treated as black...
Francis R. Bach, Michael I. Jordan
SACI
2007
IEEE
15 years 6 months ago
A Computational Intelligence Approach for Ranking Risk Factors in Preterm Birth
- The aim of this paper is to propose a filter, based on a multi-objective evolutionary algorithm, for attributes’ ranking in the context of a data mining task. The behavior of t...
Daniela Zaharie, Stefan Holban, Diana Lungeanu, Da...
MCS
2005
Springer
15 years 5 months ago
Ensemble of SVMs for Incremental Learning
Support Vector Machines (SVMs) have been successfully applied to solve a large number of classification and regression problems. However, SVMs suffer from the catastrophic forgetti...
Zeki Erdem, Robi Polikar, Fikret S. Gürgen, N...
GECCO
1999
Springer
142views Optimization» more  GECCO 1999»
15 years 4 months ago
Towards Byte Code Genetic Programming
This paper uses the GP paradigm to evolve linear genotypes (individuals) that consist of Java byte code. Our prototype GP system is implemented in Java using a standard Java devel...
Brad Harvey, James A. Foster, Deborah A. Frincke