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ICML
2007
IEEE
16 years 5 months ago
Incremental Bayesian networks for structure prediction
We propose a class of graphical models appropriate for structure prediction problems where the model structure is a function of the output structure. Incremental Sigmoid Belief Ne...
Ivan Titov, James Henderson
IJCNN
2008
IEEE
15 years 11 months ago
Evolving a neural network using dyadic connections
—Since machine learning has become a tool to make more efficient design of sophisticated systems, we present in this paper a novel methodology to create powerful neural network ...
Andreas Huemer, Mario A. Góngora, David A. ...
ACSAC
2005
IEEE
15 years 10 months ago
Exploiting Independent State For Network Intrusion Detection
Network intrusion detection systems (NIDSs) critically rely on processing a great deal of state. Often much of this state resides solely in the volatile processor memory accessibl...
Robin Sommer, Vern Paxson
IWANN
2005
Springer
15 years 10 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...
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ICML
2010
IEEE
15 years 5 months ago
Deep Supervised t-Distributed Embedding
Deep learning has been successfully applied to perform non-linear embedding. In this paper, we present supervised embedding techniques that use a deep network to collapse classes....
Martin Renqiang Min, Laurens van der Maaten, Zinen...