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» MuFeSaC: Learning When to Use Which Feature Detector
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SAC
2002
ACM
13 years 7 months ago
Color patterns for pictorial content description
In this paper, we propose a new type of image feature, which consists of patterns of colors and intensities that capture the latent associations among images and primitive feature...
Daniela Stan, Ishwar K. Sethi
ECML
2006
Springer
13 years 11 months ago
Bayesian Active Learning for Sensitivity Analysis
Abstract. Designs of micro electro-mechanical devices need to be robust against fluctuations in mass production. Computer experiments with tens of parameters are used to explore th...
Tobias Pfingsten
HICSS
2003
IEEE
120views Biometrics» more  HICSS 2003»
14 years 1 months ago
Evaluating On-line Learning Platforms: a Case Study
Our “information-oriented” society shows an increasing exigency of life-long learning. In such framework, online Learning is becoming an important tool to allow the flexibilit...
Francesco Colace, Massimo De Santo, Mario Vento
ICML
1998
IEEE
14 years 8 months ago
Feature Selection via Concave Minimization and Support Vector Machines
Computational comparison is made between two feature selection approaches for nding a separating plane that discriminates between two point sets in an n-dimensional feature space ...
Paul S. Bradley, Olvi L. Mangasarian
COLT
2006
Springer
13 years 11 months ago
A Randomized Online Learning Algorithm for Better Variance Control
We propose a sequential randomized algorithm, which at each step concentrates on functions having both low risk and low variance with respect to the previous step prediction functi...
Jean-Yves Audibert