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IJON
2006
70views more  IJON 2006»
14 years 9 months ago
A self-organizing map with homeostatic synaptic scaling
Hebbian learning has been a staple of neural-network models for many years. It is well known that the most straight-forward implementations of this popular learning rule lead to u...
Thomas J. Sullivan, Virginia R. de Sa
ESANN
2003
14 years 11 months ago
On the weight dynamics of recurrent learning
We derive continuous-time batch and online versions of the recently introduced efficient O(N2 ) training algorithm of Atiya and Parlos [2000] for fully recurrent networks. A mathem...
Ulf D. Schiller, Jochen J. Steil
ICASSP
2010
IEEE
14 years 7 months ago
Efficient online learning with individual learning-rates for phoneme sequence recognition
We describe a fast and efficient online algorithm for phoneme sequence speech recognition. Our method is using a discriminative training to update the model parameters one utteran...
Koby Crammer
CORR
2007
Springer
112views Education» more  CORR 2007»
14 years 9 months ago
Learning from compressed observations
— The problem of statistical learning is to construct a predictor of a random variable Y as a function of a related random variable X on the basis of an i.i.d. training sample fr...
Maxim Raginsky
ICML
2007
IEEE
15 years 10 months ago
Approximate maximum margin algorithms with rules controlled by the number of mistakes
We present a family of incremental Perceptron-like algorithms (PLAs) with margin in which both the "effective" learning rate, defined as the ratio of the learning rate t...
Petroula Tsampouka, John Shawe-Taylor