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TNN
2010
168views Management» more  TNN 2010»
12 years 11 months ago
On the selection of weight decay parameter for faulty networks
The weight-decay technique is an effective approach to handle overfitting and weight fault. For fault-free networks, without an appropriate value of decay parameter, the trained ne...
Andrew Chi-Sing Leung, Hongjiang Wang, John Sum
CORR
2008
Springer
179views Education» more  CORR 2008»
13 years 4 months ago
Distributed Parameter Estimation in Sensor Networks: Nonlinear Observation Models and Imperfect Communication
The paper studies the problem of distributed static parameter (vector) estimation in sensor networks with nonlinear observation models and imperfect inter-sensor communication. We...
Soummya Kar, José M. F. Moura, Kavita Raman...
IJCNN
2006
IEEE
13 years 10 months ago
Leave-One-Out Cross-Validation Based Model Selection Criteria for Weighted LS-SVMs
Abstract— While the model parameters of many kernel learning methods are given by the solution of a convex optimisation problem, the selection of good values for the kernel and r...
Gavin C. Cawley
ISCAS
2006
IEEE
84views Hardware» more  ISCAS 2006»
13 years 10 months ago
Programmable synaptic weights for an aVLSI network of spiking neurons
—We describe a spiking neuronal network which allows local synaptic weights to be assigned to individual synapses. In previous implementations of neuronal networks, the biases th...
Yingxue Wang, Shih-Chii Liu
NIPS
1990
13 years 5 months ago
Back Propagation is Sensitive to Initial Conditions
This paper explores the effect of initial weight selection on feed-forward networks learning simple functions with the back-propagation technique. We first demonstrate, through th...
John F. Kolen, Jordan B. Pollack