Nonnegative least squares learning for the random neural network

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5163 LNCS, Issue PART 1, 2008, Pages 195-204

  Timotheou, Stelios ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BATCH LEARNING; COMBINATORIAL OPTIMIZATION PROBLEMS; DISASTER MANAGEMENT; GRADIENT DESCENT ALGORITHMS; NONNEGATIVE LEAST SQUARES; OPTIMALITY; RANDOM NEURAL NETWORK;

BACKPROPAGATION; COMBINATORIAL OPTIMIZATION; DISASTER PREVENTION; LEARNING ALGORITHMS;

NEURAL NETWORKS;

EID: 58849163716     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-87536-9_21     Document Type: Conference Paper

References (19)

1. Gelenbe, E.: Random neural networks with positive and negative signals and product form solution. Neural Computation 1(4), 502-510 (1989)
2. Gelenbe, E.: Stability of the random neural network. Neural Computation 2(2), 239-247 (1990)
3. Gelenbe, E., Mao, Z.H., Li, Y.D.: Function approximation with spiked random networks. IEEE Trans. on Neural Networks 10(1), 3-9 (1999)
4. Gelenbe, E., Mao, Z.H., Li, Y.D.: Function approximation by random neural networks with a bounded number of layers. J. Differential Equations and Dynamical Systems 12(1&2), 143-170 (2004)
5. Gelenbe, E.: Learning in the recurrent random network. Neural Computation 5, 154-164 (1993)
6. Bakircioglu, H., Kocak, T.: Survey of random neural network applications. European Journal of Operational Research 126(2), 319-330 (2000)
7. Gelenbe, E., Feng, T., Krishnan, K.R.R.: Neural network methods for volumetric magnetic resonance imaging of the human brain. Proceedings of the IEEE 84(1), 1488-1496 (1996)
8. Gelenbe, E., Kocak, T.: Wafer surface reconstruction from top-down scanning electron microscope images. Microlectronic Engineering 75, 216-233 (2004)
9. Artalejo, J.R.: G-networks: A versatile approach for work removal in queueing networks. European Journal of Operational Research 126(2), 233-249 (2000)
10. Gelenbe, E.: Steady-state solution of probabilistic gene regulatory networks. Physical Review E 76(1), 031903 (2007)
11. Gelenbe, E., Timotheou, S.: Random Neural Networks with Synchronised Interactions. In: Neural Computation. MIT Press, Cambridge (2008) (accepted for publication)
12. Gelenbe, E., Timotheou, S.: Synchronised Interactions in Spiked Neuronal Networks. The Computer Journal (2008) (Oxford Journals)
13. Biegler-Konig, F., Barmann, F.: A learning algorithm for multilayered neural networks based on linear least squares problems. Neural Networks 6(1), 127-131 (1993)
14. Fontenla-Romero, O., Erdogmus, D., Principe, J.C., Alonso-Betanzos, A., Castillo, E.: Linear least-squares based methods for neural networks learning. In: Artificial Neural Networks and Neural Information Processing ICANN/ICONIP 2003, pp. 84-91 (2003)
15. Lawson, C.L., Hanson, R.J.: Solving Least Squares Problems. Prentice Hall, Englewood Cliffs (1987)
16. Bro, R., Long, S.D.: A fast non-negativity-constrained least squares algorithm. Journal of Chemometrics 11(5), 393-401 (1997)
17. Lin, C.J.: Projected gradient methods for nonnegative matrix factorization. Neural Computation 19(10), 2756-2779 (2007)
18. Bertsekas, D.: Nonlinear Programming. Athena Scientific (1995)
19. Kim, D., Sra, S., Dhillon, I.: A new projected quasi-newton approach for the nonnegative least squares problem. Technical report, Dept. of Computer Sciences. The University of Texas at Austin (December 2006)