Maximizing the zero-error density for RTRL

Proceedings of the 8th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2008

Volumn , Issue , 2008, Pages 80-84

  Alexandre, Luís A ^a  

^a UNIVERSITY OF BEIRA INTERIOR   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

CONVERGENCE RATES; ERROR VALUES; NEW APPROACHES; ONLINE LEARNING; PREDICTION PERFORMANCE; REAL-TIME RECURRENT LEARNING; SLIDING TIME WINDOWS; TIME SERIES FORECASTS; ZERO-ERROR DENSITY;

EDUCATION; ELECTRIC DIPOLE MOMENTS; ELECTRIC DISCHARGE MACHINING; INTERNET; LEARNING ALGORITHMS; MICROMACHINING; SIGNAL PROCESSING;

RECURRENT NEURAL NETWORKS;

EID: 67649844569     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISSPIT.2008.4775679     Document Type: Conference Paper

References (5)

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3. L.M. Silva, L.A. Alexandre, and J. Marques de Si. Neural network classification: Maximizing zero-error density. In Third International Conference on Advances in Pattern Recognition - ICAPR 2005, volume LNCS 3686, pages 127-135, Bath, United Kingdom, August 2005. Springer.
4. L.M. Silva, L.A. Alexandre, and J. Marques de SL New developments of the Z-EDM algorithm. In 6th International Conference on Intelligent Systems Design and Applications - ISDA 2006, volume 1, pages 1067-1072, Jinan, China, October 2006. IEEE Computer Society Press.
5. R.J. Williams and D. Zipser. A learning algorithm for continually running fully recurrent neural networks. Neural Computation, 1:270-280, 1989.