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ICCCN
2007
IEEE
13 years 7 months ago
Online Selection of Tracking Features using AdaBoost
In this paper, a novel feature selection algorithm for object tracking is proposed. This algorithm performs more robust than the previous works by taking the correlation between f...
Ying-Jia Yeh, Chiou-Ting Hsu
CVPR
2003
IEEE
14 years 9 months ago
Simultaneous Feature Selection and Classifier Training via Linear Programming: A Case Study for Face Expression Recognition
A linear programming technique is introduced that jointly performs feature selection and classifier training so that a subset of features is optimally selected together with the c...
Guodong Guo, Charles R. Dyer
SIGADA
1999
Springer
13 years 11 months ago
Redistribution in distributed Ada
In this paper we will demonstrate how Ada and its Distributed Annex may be used to relocate concurrent objects in a distributed data ow application. This relocation mechanism will...
Scott James
ICMLA
2004
13 years 9 months ago
Two new regularized AdaBoost algorithms
AdaBoost rarely suffers from overfitting problems in low noise data cases. However, recent studies with highly noisy patterns clearly showed that overfitting can occur. A natural s...
Yijun Sun, Jian Li, William W. Hager
NIPS
2001
13 years 9 months ago
Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade
This paper develops a new approach for extremely fast detection in domains where the distribution of positive and negative examples is highly skewed (e.g. face detection or databa...
Paul A. Viola, Michael J. Jones