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NIPS
2003
13 years 9 months ago
Kernel Dimensionality Reduction for Supervised Learning
We propose a novel method of dimensionality reduction for supervised learning. Given a regression or classification problem in which we wish to predict a variable Y from an expla...
Kenji Fukumizu, Francis R. Bach, Michael I. Jordan
ECIS
2000
13 years 9 months ago
IT-Enabled Sophistication Banking
- Globalization of financial markets resulting from both IT (particularly internet standards) and increasing homogeneity of regulation has strongly affected the environment, financ...
Hans Ulrich Buhl, Dennis Kundisch, Andreas Leinfel...
UAI
1998
13 years 9 months ago
Large Deviation Methods for Approximate Probabilistic Inference
We study two-layer belief networks of binary random variables in which the conditional probabilities Pr childjparents depend monotonically on weighted sums of the parents. In larg...
Michael J. Kearns, Lawrence K. Saul
UAI
1990
13 years 9 months ago
separable and transitive graphoids
We examine the notion of "unrelatedness" in a probabilistic framework. Three formulations are presented. In the first formulation, two variables a and b are totally inde...
Dan Geiger, David Heckerman
ICPR
2010
IEEE
13 years 8 months ago
The Problem of Fragile Feature Subset Preference in Feature Selection Methods and a Proposal of Algorithmic Workaround
Abstract—We point out a problem inherent in the optimization scheme of many popular feature selection methods. It follows from the implicit assumption that higher feature selecti...
Petr Somol, Jiri Grim, Pavel Pudil