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ICML
2000
IEEE
14 years 5 months ago
FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness
Most machine learning algorithms are lazy: they extract from the training set the minimum information needed to predict its labels. Unfortunately, this often leads to models that ...
Joseph O'Sullivan, John Langford, Rich Caruana, Av...
MVA
2002
195views Computer Vision» more  MVA 2002»
13 years 4 months ago
Improved Adaptive Mixture Learning for Robust Video Background Modeling
2 Related Works Gaussian mixtures are often used for data modeling in many real-time applications such as video background modeling and speaker direction tracking. The real-time a...
Dar-Shyang Lee
PRIS
2001
13 years 6 months ago
Improving the Robustness of Parametric Shape Tracking with Switched Multiple Models
This paper addresses the problem of tracking objects with complex motion dynamics or shape changes. It is assumed that some of the visual features detected in the image (e.g., edg...
Jacinto C. Nascimento, Jorge S. Marques
SIGIR
2012
ACM
11 years 7 months ago
Robust ranking models via risk-sensitive optimization
Many techniques for improving search result quality have been proposed. Typically, these techniques increase average effectiveness by devising advanced ranking features and/or by...
Lidan Wang, Paul N. Bennett, Kevyn Collins-Thompso...
CORR
2011
Springer
209views Education» more  CORR 2011»
12 years 8 months ago
Analysis and Improvement of Low Rank Representation for Subspace segmentation
We analyze and improve low rank representation (LRR), the state-of-the-art algorithm for subspace segmentation of data. We prove that for the noiseless case, the optimization mode...
Siming Wei, Zhouchen Lin