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IJON
2006
70views more  IJON 2006»
15 years 3 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
15 years 4 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
15 years 1 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
113
Voted
CORR
2007
Springer
112views Education» more  CORR 2007»
15 years 3 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
145
Voted
ICML
2007
IEEE
16 years 4 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