Supervised learning with restricted training sets: A generating functional analysis

Journal of Physics A: Mathematical and General

Volumn 34, Issue 42, 2001, Pages 9009-9026

  Heimel, J A F ^a   Coolen, A C C ^a  

^a KING'S COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0035955590     PISSN: 03054470     EISSN: None     Source Type: Journal    
DOI: 10.1088/0305-4470/34/42/318     Document Type: Article

References (24)

1. Kinzel W and Rujan P 1990 Improving a network generalization ability by selecting examples Europhys. Lett. 13 473
2. Biehl M and Schwarze H 1992 On-line learning of a time-dependent rule Europhys. Lett. 20 773
3. Biehl M and Riegler P 1994 On-line learning with a perceptron Europhys. Lett. 28 525
4. Rae H C, Heimel J A F and Coolen A C C 2000 Non-deterministic learning dynamics in large neural networks due to structural data bias J. Phys. A: Math. Gen. 33 8703
5. Hertz J A, Krogh A and Thorbergsson G I 1989 Phase transitions in simple learning J. Phys. A: Math. Gen. 22 2133
6. Rae H C, Sollich P and Coolen A C C 1999 On-line learning with restricted training sets: An exactly solvable case J. Phys. A: Math. Gen. 32 3321
7. Sollich P and Barber D 1998 Online learning from finite training sets and robustness to input bias Neural Computation 10 2201
8. Heskes T M and Kappen B 1991 Learning processes in neural networks Phys. Rev. A 44 2718
9. Hansen L K, Pathria R and Salamon P 1993 Stochastic dynamics of supervised learning J. Phys. A: Math. Gen. 2663
10. Radons G 1993 On stochastic dynamics of supervised learning J. Phys. A: Math. Gen. 26 3455
11. Heskes T M 1994 On Fokker-Planck approximations of on-line learning processes J. Phys. A: Math. Gen. 27 5145
12. Barber D and Sollich P 1998 On-Line Learning in Neural Networks ed D Saad (Cambridge: Cambridge University Press) pp 279-302
13. Mézard M, Parisi G and Virasoro M A 1987 Spin Glass Theory and Beyond (Singapore: World Scientific)
14. Horner H 1992 Dynamics of learning for the binary perceptron problem Z. Phys. B 86 291
15. Horner H 1992 Dynamics of learning and generalization in a binary perceptron model Z. Phys. B 87 371
16. Li S and Wong K Y M 2000 Advances in Neural Information Processing vol 12 ed S A Solla and T K Leen (Cambridge, MA: MIT Press)
17. Wong K Y M, Li S and Tong Y W 2000 Many-body approach to the dynamics of batch learning Phys. Rev. E 62 4036
18. Wong K Y M, Li S and Luo P 2001 Advanced Mean Field Methods-Theory and Practice ed M Opper and D Saad (Cambridge, MA: MIT Press)
19. Coolen A C C and Saad D 1998 On-Line Learning in Neural Networks ed D Saad (Cambridge: Cambridge University Press) pp 303-43
20. Coolen A C C and Saad D 2000 Dynamics of learning with restricted training sets Phys. Rev. E 62 5444
21. Coolen A C C, Saad D and Xiong Y S 2000 On-line learning from restricted training sets in multilayer neural networks Europhys. Lett. 51 691
22. Mace C W H and Coolen A C C 2000 Advances in Neural Information Processing vol 12 ed S A Solla, T K Leen and K Müller (Cambridge, MA: MIT Press) p 237
23. Eissfeller H and Opper M 1992 New method for studying the dynamics of disordered spin systems without finite-size effects Phys. Rev. Lett. 68 (13) 2094
24. Kinouchi O and Caticha N 1992 Optimal generalization in perceptrons J. Phys. A: Math. Gen. 25 6243