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AI
2002
Springer
14 years 11 months ago
Ensembling neural networks: Many could be better than all
Neural network ensemble is a learning paradigm where many neural networks are jointly used to solve a problem. In this paper, the relationship between the ensemble and its compone...
Zhi-Hua Zhou, Jianxin Wu, Wei Tang
ANNPR
2006
Springer
15 years 1 months ago
A Convolutional Neural Network Tolerant of Synaptic Faults for Low-Power Analog Hardware
Abstract. Recently, the authors described a training method for a convolutional neural network of threshold neurons. Hidden layers are trained by by clustering, in a feed-forward m...
Johannes Fieres, Karlheinz Meier, Johannes Schemme...
ICONIP
1998
15 years 1 months ago
A Web Oriented Recurrent Neural Network Simulator
YANNS (Yet Another Neural Network Simulator) is a new object-oriented neural network simulator for feedforward networks as well as general recurrent networks. The goal of this pro...
Romuald Boné, Michel Crucianu, Pascal Makri...
ICANN
2009
Springer
15 years 4 months ago
An EM Based Training Algorithm for Recurrent Neural Networks
Recurrent neural networks serve as black-box models for nonlinear dynamical systems identification and time series prediction. Training of recurrent networks typically minimizes t...
Jan Unkelbach, Yi Sun, Jürgen Schmidhuber
ISNN
2009
Springer
15 years 6 months ago
Use of Ensemble Based on GA for Imbalance Problem
In real-world applications, it has been observed that class imbalance (significant differences in class prior probabilities) may produce an important deterioration of the classifie...
Laura Cleofas, Rosa Maria Valdovinos, Vicente Garc...