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TNN
2008
177views more  TNN 2008»
13 years 7 months ago
Adaptive Importance Sampling to Accelerate Training of a Neural Probabilistic Language Model
Previous work on statistical language modeling has shown that it is possible to train a feed-forward neural network to approximate probabilities over sequences of words, resulting...
Yoshua Bengio, Jean-Sébastien Senecal
PPSN
1992
Springer
13 years 12 months ago
Structure Evolution and Incomplete Induction
We present an application of arti cial neural networks to machine condition monitoring. Since several signal preprocessing methods produce high dimensional feature vectors there i...
Reinhard Lohmann
IJCNN
2006
IEEE
14 years 1 months ago
Fast Modifications of the SpikeProp Algorithm
Abstract - In this paper we develop and analyze Spiking Neural Network (SNN) versions of Resilient Propagation (RProp) and QuickProp, both training methods used to speed up trainin...
Sam McKennoch, Dingding Liu, Linda G. Bushnell
IEAAIE
2004
Springer
14 years 1 months ago
Data Mining Approach for Analyzing Call Center Performance
Abstract. The aim of our research was to apply well-known data mining techniques (such as linear neural networks, multi-layered perceptrons, probabilistic neural networks, classifi...
Marcin Paprzycki, Ajith Abraham, Ruiyuan Guo, Srin...
FPL
1994
Springer
170views Hardware» more  FPL 1994»
13 years 12 months ago
A Fast FPGA Implementation of a General Purpose Neuron
The implementation of larger digital neural networks has not been possible due to the real-estate requirements of single neurons. We present an expandable digital architecture whic...
Valentina Salapura, Michael Gschwind, Oliver Maisc...