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» Training Methods for Adaptive Boosting of Neural Networks
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CVPR
2010
IEEE
15 years 3 months ago
Multi-Target Tracking by On-Line Learned Discriminative Appearance Models
We present an approach for online learning of discriminative appearance models for robust multi-target tracking in a crowded scene from a single camera. Although much progress has...
Cheng-Hao Kuo, Chang Huang, Ram Nevatia
IWANN
2005
Springer
15 years 3 months ago
Bias and Variance of Rotation-Based Ensembles
Abstract. In Machine Learning, ensembles are combination of classifiers. Their objective is to improve the accuracy. In previous works, we have presented a method for the generati...
Juan José Rodríguez, Carlos J. Alons...
PAMI
2008
173views more  PAMI 2008»
14 years 9 months ago
Bayes Classification of Online Arabic Characters by Gibbs Modeling of Class Conditional Densities
This study investigates Bayes classification of online Arabic characters using histograms of tangent differences and Gibbs modeling of the class-conditional probability density fun...
Neila Mezghani, Amar Mitiche, Mohamed Cheriet
92
Voted
ESANN
2006
14 years 11 months ago
Magnification control for batch neural gas
Neural gas (NG) constitutes a very robust clustering algorithm which can be derived as stochastic gradient descent from a cost function closely connected to the quantization error...
Barbara Hammer, Alexander Hasenfuss, Thomas Villma...
87
Voted
JMLR
2010
107views more  JMLR 2010»
14 years 4 months ago
Learning Instance-Specific Predictive Models
This paper introduces a Bayesian algorithm for constructing predictive models from data that are optimized to predict a target variable well for a particular instance. This algori...
Shyam Visweswaran, Gregory F. Cooper