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» Online Selection of Tracking Features using AdaBoost
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ICCV
2003
IEEE
15 years 11 months ago
On-Line Selection of Discriminative Tracking Features
This paper presents a method for evaluating multiple feature spaces while tracking, and for adjusting the set of features used to improve tracking performance. Our hypothesis is t...
Robert T. Collins, Yanxi Liu
ISNN
2005
Springer
15 years 2 months ago
Neural Network Based Online Feature Selection for Vehicle Tracking
Abstract. Aiming at vehicle tracking with a single moving camera for autonomous driving, this paper presents a strategy of online feature selection combined with related process fr...
Tie Liu, Nanning Zheng, Hong Cheng
WWW
2008
ACM
15 years 10 months ago
Floatcascade learning for fast imbalanced web mining
This paper is concerned with the problem of Imbalanced Classification (IC) in web mining, which often arises on the web due to the "Matthew Effect". As web IC applicatio...
Xiaoxun Zhang, Xueying Wang, Honglei Guo, Zhili Gu...
118
Voted
ESANN
2006
14 years 10 months ago
Random Forests Feature Selection with K-PLS: Detecting Ischemia from Magnetocardiograms
Random Forests were introduced by Breiman for feature (variable) selection and improved predictions for decision tree models. The resulting model is often superior to AdaBoost and ...
Long Han, Mark J. Embrechts, Boleslaw K. Szymanski...
93
Voted
ICPR
2008
IEEE
15 years 3 months ago
A hybrid approach to gender classification from face images
Recently, gender classification from face images has attracted a great deal of attention. It can be useful in many places. In this paper, a novel hybrid face coding method by fusi...
Ziyi Xu, Li Lu, Pengfei Shi