Improved p-Delta learning algorithm

International Conference on Systems - Proceedings

Volumn 1, Issue , 2010, Pages 282-287

  Mirsu, R ^a   Tiponut, V ^a   Petromanjanc, L ^a   Haraszy, Z ^a  

^a POLITEHNICA UNIVERSITY OF TIMISOARA   (Romania)

Author keywords

Adaptive learning; Liquid state machines; Noise margin; Parallel perceptrons; Perceptron delta learning rule; Perceptrons

Indexed keywords

ADAPTIVE LEARNING; LIQUID STATE MACHINES; NOISE MARGINS; PARALLEL PERCEPTRONS; PERCEPTRON;

ADAPTIVE ALGORITHMS; CYBERNETICS; PATTERN RECOGNITION SYSTEMS;

LEARNING ALGORITHMS;

EID: 79958750273     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (5)

1. Peter Auer, Harald M. Burgsteiner, Wolfgang Maass. The p-Delta Learning Rule for Parallel Perceptron, 2002.
2. Wolgang Maass, Thomas Natschlager, Henry Markram. Real-Time Computing without stable states: A new Framework for Neural Computation based on Perturbation, 2001.
3. Wulfram Gerstner, Coding Properties of Spiking Neurons: reverse and cross-correlations. Neural Networks, Vol.14, Lausanne, 2001, pp. 599-610.
4. Freund Y., Schapire R. E. Large margin classification using the Perceptron algorithm. Machine Learning, 37(3), 1999, pp. 277-296. (Pubitemid 32210619)
5. Wolfgang Maass. Neural Computation with Winner-Take-All as the only Nonlinear Operation, Advances in Neural Information Processing Systems, vol. 12, MIT Press, Cambridge, 2000, pp. 293-299.