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TNN
2008
93views more  TNN 2008»
13 years 8 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
14 years 3 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, ...
ICANN
2007
Springer
14 years 2 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,...
IJCNN
2006
IEEE
14 years 2 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
ACMSE
2008
ACM
13 years 10 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