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140
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TNN
2008
93views more  TNN 2008»
15 years 3 months ago
Towards the Optimal Design of Numerical Experiments
This paper addresses the problem of the optimal design of numerical experiments for the construction of nonlinear surrogate models. We describe a new method, called learner disagre...
S. Gazut, J.-M. Martinez, Gérard Dreyfus, Y...
DSOM
2009
Springer
15 years 10 months ago
Hidden Markov Model Modeling of SSH Brute-Force Attacks
Abstract. Nowadays, network load is constantly increasing and high-speed infrastructures (1-10Gbps) are becoming increasingly common. In this context, flow-based intrusion detecti...
Anna Sperotto, Ramin Sadre, Pieter-Tjerk de Boer, ...
101
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ICANN
2007
Springer
15 years 10 months ago
Solving Deep Memory POMDPs with Recurrent Policy Gradients
Abstract. This paper presents Recurrent Policy Gradients, a modelfree reinforcement learning (RL) method creating limited-memory stochastic policies for partially observable Markov...
Daan Wierstra, Alexander Förster, Jan Peters,...
118
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IJCNN
2006
IEEE
15 years 9 months ago
Improving the Convergence of Backpropagation by Opposite Transfer Functions
—The backpropagation algorithm is a very popular approach to learning in feed-forward multi-layer perceptron networks. However, in many scenarios the time required to adequately ...
Mario Ventresca, Hamid R. Tizhoosh
130
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ACMSE
2008
ACM
15 years 5 months ago
Training approaches in neural enhancement for multiobjective optimization
In previous work, a neural network was used to increase the number of solutions found by an evolutionary multiobjective optimization algorithm. In this paper, various approaches a...
Aaron Garrett, Gerry V. Dozier