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» Training Neural Networks with GA Hybrid Algorithms
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IJCNN
2008
IEEE
15 years 8 months ago
Numerical condition of feedforward networks with opposite transfer functions
— Numerical condition affects the learning speed and accuracy of most artificial neural network learning algorithms. In this paper, we examine the influence of opposite transfe...
Mario Ventresca, Hamid R. Tizhoosh
ICASSP
2011
IEEE
14 years 5 months ago
Extensions of recurrent neural network language model
We present several modifications of the original recurrent neural network language model (RNN LM). While this model has been shown to significantly outperform many competitive l...
Tomas Mikolov, Stefan Kombrink, Lukas Burget, Jan ...
IJCNN
2008
IEEE
15 years 8 months ago
A comparison of architectural varieties in Radial Basis Function Neural Networks
— Representation of knowledge within a neural model is an active field of research involved with the development of alternative structures, training algorithms, learning modes an...
Mehmet Önder Efe, Cosku Kasnakoglu
CVPR
2012
IEEE
13 years 4 months ago
Image denoising: Can plain neural networks compete with BM3D?
Image denoising can be described as the problem of mapping from a noisy image to a noise-free image. The best currently available denoising methods approximate this mapping with c...
Harold Christopher Burger, Christian J. Schuler, S...
CNSR
2004
IEEE
174views Communications» more  CNSR 2004»
15 years 5 months ago
Network Intrusion Detection Using an Improved Competitive Learning Neural Network
This paper presents a novel approach for detecting network intrusions based on a competitive learning neural network. In the paper, the performance of this approach is compared to...
John Zhong Lei, Ali A. Ghorbani