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CVPR
2009
IEEE
15 years 2 months ago
Regularized Multi-Class Semi-Supervised Boosting
Many semi-supervised learning algorithms only deal with binary classification. Their extension to the multi-class problem is usually obtained by repeatedly solving a set of bina...
Amir Saffari, Christian Leistner, Horst Bischof
ATAL
2008
Springer
13 years 8 months ago
Efficient multi-agent reinforcement learning through automated supervision
Multi-Agent Reinforcement Learning (MARL) algorithms suffer from slow convergence and even divergence, especially in large-scale systems. In this work, we develop a supervision fr...
Chongjie Zhang, Sherief Abdallah, Victor R. Lesser
NIPS
1994
13 years 8 months ago
Efficient Methods for Dealing with Missing Data in Supervised Learning
We present efficient algorithms for dealing with the problem of missing inputs (incomplete feature vectors) during training and recall. Our approach is based on the approximation ...
Volker Tresp, Ralph Neuneier, Subutai Ahmad
ANNPR
2008
Springer
13 years 8 months ago
Supervised Incremental Learning with the Fuzzy ARTMAP Neural Network
Abstract. Automatic pattern classifiers that allow for on-line incremental learning can adapt internal class models efficiently in response to new information without retraining fr...
Jean-François Connolly, Eric Granger, Rober...
CVPR
2006
IEEE
14 years 8 months ago
Learning Semantic Patterns with Discriminant Localized Binary Projections
In this paper, we present a novel approach to learning semantic localized patterns with binary projections in a supervised manner. The pursuit of these binary projections is refor...
Shuicheng Yan, Tianqiang Yuan, Xiaoou Tang