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» Multilayer Perceptron: Architecture Optimization and Trainin...
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NIPS
1996
15 years 7 months ago
Early Stopping-But When?
Abstract. Validation can be used to detect when over tting starts during supervised training of a neural network; training is then stopped before convergence to avoid the over ttin...
Lutz Prechelt
TNN
2010
234views Management» more  TNN 2010»
15 years 11 days 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
TNN
2008
181views more  TNN 2008»
15 years 5 months ago
Optimized Approximation Algorithm in Neural Networks Without Overfitting
In this paper, an optimized approximation algorithm (OAA) is proposed to address the overfitting problem in function approximation using neural networks (NNs). The optimized approx...
Yinyin Liu, Janusz A. Starzyk, Zhen Zhu
TCIAIG
2010
15 years 11 days ago
Modeling Player Experience for Content Creation
In this paper, we use computational intelligence techniques to built quantitative models of player experience for a platform game. The models accurately predict certain key affecti...
Christopher Pedersen, Julian Togelius, Georgios N....
IJCSA
2007
100views more  IJCSA 2007»
15 years 5 months ago
Using Artificial Neural networks for the modelling of a distillation column
The main aim of this paper is to establish a reliable model both for the steady-state and unsteady-state regimes of a nonlinear process. The use of this model should reflect the t...
Yahya Chetouani