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IJCNN
2006
IEEE
15 years 3 months ago
Adaptation of Artificial Neural Networks Avoiding Catastrophic Forgetting
— In connectionist learning, one relevant problem is “catastrophic forgetting” that may occur when a network, trained with a large set of patterns, has to learn new input pat...
Dario Albesano, Roberto Gemello, Pietro Laface, Fr...
ICES
2010
Springer
277views Hardware» more  ICES 2010»
14 years 7 months ago
An Efficient, High-Throughput Adaptive NoC Router for Large Scale Spiking Neural Network Hardware Implementations
Recently, a reconfigurable and biologically inspired paradigm based on network-on-chip (NoC) and spiking neural networks (SNNs) has been proposed as a new method of realising an ef...
Snaider Carrillo, Jim Harkin, Liam McDaid, Sandeep...
ICANN
2005
Springer
15 years 3 months ago
Mutual Information and k-Nearest Neighbors Approximator for Time Series Prediction
This paper presents a method that combines Mutual Information and k-Nearest Neighbors approximator for time series prediction. Mutual Information is used for input selection. K-Nea...
Antti Sorjamaa, Jin Hao, Amaury Lendasse
JDCTA
2008
117views more  JDCTA 2008»
14 years 9 months ago
Efficiency Enhancement of Neural Network with Phase Only Correlation
The research work is carried out to enhance the recognition accuracy of neural network with Phase Only Correlation (POC) and reduce the time required for POC by refining its input...
Usman Qayyum
IJCNN
2000
IEEE
15 years 2 months ago
Continuous Optimization of Hyper-Parameters
Many machine learning algorithms can be formulated as the minimization of a training criterion which involves (1) \training errors" on each training example and (2) some hype...
Yoshua Bengio