Annealed RNN learning of finite state automata

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

Volumn 1112 LNCS, Issue , 1996, Pages 519-524

  Arai, Ken Ichi ^a   Nakano, Ryohei ^a  

^a NTT Communication Science Laboratories   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

RECURRENT NEURAL NETWORKS; SCHEDULING; AUTOMATA THEORY; NEURAL NETWORKS;

LEARNING METHODS; LOCAL MINIMUMS; RECURRENT NEURAL NETWORK (RNN); STATE REPRESENTATION; BETA LEARNING;

ANNEALING;

EID: 84902201900     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-61510-5_89     Document Type: Conference Paper

References (8)

1. J. Hertz, A. Krogh and R.G. Palmer, Introduction to the Theory of Neural Computation, Addison-Wesley (1991).
2. Leong K.L., Fixed Point Analysis for Discrete-Time Recurrent Neural Networks, Proc. IJCNN IV (1992) 134-139.
3. P. Tiño, B.G. Home, C.L. Giles and P.C. Collingwood, Finite State Machines and Recurrent Neural Networks - Automata and Dynamical System Approaches, UMIACS-TR-95-1, CS-TR-3396 (1995).
4. M.W. Goudreau, C.L. Giles, S.T. Chakradhar and D. Chen, First-Order Versus Second-Order Single-Layer Recurrent Neural Networks, IEEE Neural Networks, Vol. 5 No. 3 (1994) 511-111.
5. P. Manolios and R. Fanelli, First-Order Recurrent Neural Networks and Deterministic Finite State Automata, Neural Computation, Vol. 6 (1994) 1155-1173.
6. S. Das, C.L. Giles and G.Z. Sun, Using Prior Knowledge in a NNPDA to Learn Context-Free Languages, Advances in Neural Information Processing Systems, Vol. 5 (1993) 65-71.
7. D.E. Rumelhart, G.E. Hinton and R.J. Williams, Learning Internal Representations by Error Propagation, in Parallel Distributed Processing, D.E. Rumelhart and J.L. McClelland (1986) Vol. 1 318-362.
8. N. Ueda and R. Nakano, Deterministic Annealing Variant of the EM Algorithm, Advances in Neural Information Processing Systems, Vol. 7 (1995) 545-552.