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» Learning Algorithms for Domain Adaptation
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CVPR
2006
IEEE
14 years 10 months ago
Supervised Learning of Edges and Object Boundaries
Edge detection is one of the most studied problems in computer vision, yet it remains a very challenging task. It is difficult since often the decision for an edge cannot be made ...
Piotr Dollár, Zhuowen Tu, Serge Belongie
NIPS
2004
13 years 10 months ago
Boosting on Manifolds: Adaptive Regularization of Base Classifiers
In this paper we propose to combine two powerful ideas, boosting and manifold learning. On the one hand, we improve ADABOOST by incorporating knowledge on the structure of the dat...
Balázs Kégl, Ligen Wang
ICRA
2008
IEEE
229views Robotics» more  ICRA 2008»
14 years 3 months ago
Learning of moving cast shadows for dynamic environments
Abstract— We propose a novel online framework for detecting moving shadows in video sequences using statistical learning techniques. In this framework, Support Vector Machines ar...
Ajay J. Joshi, Nikolaos Papanikolopoulos
IJIT
2004
13 years 10 months ago
Improving the Convergence of the Backpropagation Algorithm Using Local Adaptive Techniques
Since the presentation of the backpropagation algorithm, a vast variety of improvements of the technique for training a feed forward neural networks have been proposed. This articl...
Z. Zainuddin, N. Mahat, Y. Abu Hassan
STAIRS
2008
109views Education» more  STAIRS 2008»
13 years 10 months ago
Representing Case Variations for Learning General and Specific Adaptation Rules
Adaptation is a task of case-based reasoning systems that is largely domain-dependant. This motivates the study of adaptation knowledge acquisition (AKA) that can be carried out th...
Fadi Badra, Jean Lieber