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» Evaluating learning algorithms and classifiers
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140
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JMLR
2008
230views more  JMLR 2008»
15 years 15 days ago
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of...
Michael Collins, Amir Globerson, Terry Koo, Xavier...
108
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EOR
2007
99views more  EOR 2007»
15 years 14 days ago
Learning lexicographic orders
The purpose of this paper is to learn the order of criteria of lexicographic decision under various reasonable assumptions. We give a sample evaluation and an oracle based algorit...
József Dombi, Csanád Imreh, Ná...
127
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KAIS
2008
150views more  KAIS 2008»
15 years 15 days ago
A survey on algorithms for mining frequent itemsets over data streams
The increasing prominence of data streams arising in a wide range of advanced applications such as fraud detection and trend learning has led to the study of online mining of freq...
James Cheng, Yiping Ke, Wilfred Ng
TNN
2010
234views Management» more  TNN 2010»
14 years 7 months ago
Novel maximum-margin training algorithms for supervised neural networks
This paper proposes three novel training methods, two of them based on the back-propagation approach and a third one based on information theory for Multilayer Perceptron (MLP) bin...
Oswaldo Ludwig, Urbano Nunes
111
Voted
CIDM
2009
IEEE
15 years 4 months ago
Empirical comparison of graph classification algorithms
The graph classification problem is learning to classify separate, individual graphs in a graph database into two or more categories. A number of algorithms have been introduced fo...
Nikhil S. Ketkar, Lawrence B. Holder, Diane J. Coo...