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Learning Graph Matching
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Tibério S. Caetano, Julian J. McAuley, Li Cheng, Quoc V. Le, Alex J. Smola
Issue Date:June 2009
pp. 1048-1058
As a fundamental problem in pattern recognition, graph matching has applications in a variety of fields, from computer vision to computational biology. In graph matching, patterns are modeled as graphs and pattern recognition amounts to finding a correspon...
 
Learning Graph Matching
Found in: Computer Vision, IEEE International Conference on
By Tiberio S. Caetano, Li Cheng, Quoc V. Le, Alex J. Smola
Issue Date:October 2007
pp. 1-8
As a fundamental problem in pattern recognition, graph matching has found a variety of applications in the field of computer vision. In graph matching, patterns are modeled as graphs and pattern recognition amounts to finding a correspondence between the n...
 
Proximal regularization for online and batch learning
Found in: Proceedings of the 26th Annual International Conference on Machine Learning (ICML '09)
By Chuan-Sheng Foo, Chuong B. Do, Quoc V. Le
Issue Date:June 2009
pp. 1-8
Many learning algorithms rely on the curvature (in particular, strong convexity) of regularized objective functions to provide good theoretical performance guarantees. In practice, the choice of regularization penalty that gives the best testing set perfor...
     
Estimating labels from label proportions
Found in: Proceedings of the 25th international conference on Machine learning (ICML '08)
By Alex J. Smola, Novi Quadrianto, Quoc V. Le, Tiberio S. Caetano
Issue Date:July 2008
pp. 776-783
Consider the following problem: given sets of unlabeled observations, each set with known label proportions, predict the labels of another set of observations, also with known label proportions. This problem appears in areas like e-commerce, spam filtering...
     
Simpler knowledge-based support vector machines
Found in: Proceedings of the 23rd international conference on Machine learning (ICML '06)
By Alex J. Smola, Quoc V. Le, Thomas Gartner
Issue Date:June 2006
pp. 521-528
If appropriately used, prior knowledge can significantly improve the predictive accuracy of learning algorithms or reduce the amount of training data needed. In this paper we introduce a simple method to incorporate prior knowledge in support vector machin...
     
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