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JMLR
2006
140views more  JMLR 2006»
13 years 9 months ago
Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error
The goal of active learning is to determine the locations of training input points so that the generalization error is minimized. We discuss the problem of active learning in line...
Masashi Sugiyama
CVPR
2006
IEEE
14 years 12 months ago
Learning Boosted Asymmetric Classifiers for Object Detection
Object detection can be posted as those classification tasks where the rare positive patterns are to be distinguished from the enormous negative patterns. To avoid the danger of m...
Xinwen Hou, Cheng-Lin Liu, Tieniu Tan
ICIP
2003
IEEE
14 years 11 months ago
Boosting linear discriminant analysis for face recognition
In this paper, we propose a new algorithm to boost performance of traditional Linear Discriminant Analysis (LDA)-based face recognition (FR) methods in complex FR tasks, where hig...
Juwei Lu, Konstantinos N. Plataniotis, Anastasios ...
KDD
2007
ACM
190views Data Mining» more  KDD 2007»
14 years 10 months ago
Model-shared subspace boosting for multi-label classification
Typical approaches to multi-label classification problem require learning an independent classifier for every label from all the examples and features. This can become a computati...
Rong Yan, Jelena Tesic, John R. Smith
ACCV
2006
Springer
14 years 3 months ago
Boosted Algorithms for Visual Object Detection on Graphics Processing Units
Nowadays, the use of machine learning methods for visual object detection has become widespread. Those methods are robust. They require an important processing power and a high mem...
Hicham Ghorayeb, Bruno Steux, Claude Laurgeau