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» Framework for Agile Methods Classification
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ICCV
2009
IEEE
13 years 6 months ago
A robust boosting tracker with minimum error bound in a co-training framework
The varying object appearance and unlabeled data from new frames are always the challenging problem in object tracking. Recently machine learning methods are widely applied to tra...
Rong Liu, Jian Cheng, Hanqing Lu
TNN
2008
119views more  TNN 2008»
13 years 8 months ago
Selecting Useful Groups of Features in a Connectionist Framework
Abstract--Suppose for a given classification or function approximation (FA) problem data are collected using sensors. From the output of the th sensor, features are extracted, ther...
Debrup Chakraborty, Nikhil R. Pal
IJON
2006
103views more  IJON 2006»
13 years 8 months ago
Kernel extrapolation
We present a framework for efficient extrapolation of reduced rank approximations, graph kernels, and locally linear embeddings (LLE) to unseen data. We also present a principled ...
S. V. N. Vishwanathan, Karsten M. Borgwardt, Omri ...
CVPR
2008
IEEE
14 years 10 months ago
On the use of independent tasks for face recognition
We present a method for learning discriminative linear feature extraction using independent tasks. More concretely, given a target classification task, we consider a complementary...
Àgata Lapedriza, David Masip, Jordi Vitri&a...
CORR
2008
Springer
108views Education» more  CORR 2008»
13 years 9 months ago
Hierarchical Bag of Paths for Kernel Based Shape Classification
Graph kernels methods are based on an implicit embedding of graphs within a vector space of large dimension. This implicit embedding allows to apply to graphs methods which where u...
François-Xavier Dupé, Luc Brun