The intrinsic recurrent support vector machine

ESANN 2007 Proceedings - 15th European Symposium on Artificial Neural Networks

Volumn , Issue , 2007, Pages 325-330

  Schneegaß, Daniel ^a,b   Schaefer, Anton Maximilian ^a,c   Martinetz, Thomas ^b  

^a SIEMENS AG   (Germany)

^b UNIVERSITY OF LÜBECK   (Germany)

^c UNIVERSITY OF OSNABRÜCK   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMIC SOLUTIONS; OPTIMISATION PROBLEMS; RECURRENT SUPPORT VECTOR MACHINES;

ALGORITHMS; DYNAMICAL SYSTEMS; NEURAL NETWORKS;

ELECTRIC LOAD FORECASTING;

EID: 84866866431     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

1. Nello Cristianini and John Shawe-Taylor. Support Vector Machines And Other Kernelbased Learning Methods. Cambridge University Press, Cambridge, 2000.
2. John C. Platt. Fast training of support vector machines using sequential minimal optimization. In Advances in kernel methods: support vector learning, pages 185-208. MIT Press, Cambridge, MA, USA, 1999.
3. S. Sathiya Keerthi, Shirish Krishnaj Shevade, Chiranjib Bhattacharyya, and K. R. K. Murthy. Improvements to platt's smo algorithm for svm classifier design. Neural Computation, 13(3):637-649, 2001.
4. J. A. K. Suykens and J. Vandewalle. Recurrent least squares support vector machines. IEEE Transactions on Circuits Systems, 47:1109-1114, 2000.
5. Juergen Schmidhuber, Matteo Gagliolo, Daan Wierstra, and Faustino Gomez. Evolino for recurrent support vector machines. In Proc. of the European Symposium on Artificial Neural Networks, pages 593-598, 2006.
6. H. G. Zimmermann and R. Neuneier. A Field Guide to Dynamical Recurrent Networks, chapter Neural Network Architectures for the Modeling of Dynamical Systems, pages 311-350. 2001.
7. Chih-Chung Chang and Chih-Jen Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm.