Evaluating the air-sea interactions and fluxes using an instance-based reasoning system

AI Communications

Volumn 18, Issue 4, 2005, Pages 247-256

  Corchado, Juan M ^a   Aiken, Jim ^b   Corchado, Emilio S ^c   Fdez Riverola, Florentino ^d  

^a UNIVERSITY OF SALAMANCA   (Spain)

^b PLYMOUTH MARINE LABORATORY   (United Kingdom)

^c UNIVERSITY OF BURGOS   (Spain)

^d UNIVERSITY OF VIGO   (Spain)

Author keywords

Application; CBR; Hebbian learning and clustering; Instance based reasoning system

Indexed keywords

CLIMATE CHANGE; CLIMATOLOGY; LEARNING SYSTEMS; OCEANOGRAPHY;

AIR-SEA INTERACTIONS; INSTANCE-BASED REASONING SYSTEM;

CARBON DIOXIDE;

EID: 32444450552     PISSN: 09217126     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (28)

1. W.G. Finnegan, R.L. Pitter and L.G. Young, Atmospheric Environment A25 (1991), 2531-2534.
2. J.L. Sarmiento and M. Dender, Carbon biogeochemistry and climate change, Photosynthesis Research 39 (1994), 209-234.
3. I. Watson and F. Marir, Case-based reasoning: A review, The Knowledge Engineering Review 9(3) (1994).
4. S.K. Pal, T.S. Dillon and D.S. Yeung, eds, Soft Computing in Case-based Reasoning, Springer, London, UK, 2000.
5. C. Fyfe and J.M. Corchado, Automating the construction of CBR systems using Kernel Methods, International Journal of Intelligent Systems 16(4) (2001).
6. J.M. Corchado, E.S. Corchado, J. Aiken, C. Fyfe, F. Fdez-Riverola and M. Glez-Bedia, Maximum Likelihood Hebbian Learning based retrieval method for CBR systems, in: 5th International Conference on Case-Based Reasoning, Springer, Trondheim, Norway, 2003.
7. F. Fdez-Riverola and J.M. Corchado, Forecasting red tides using an hybrid neuro-symbolic system, AICOM 16(4) (2003), 221-233.
8. 2 budget with a CoHeLIBR system, in: 7th European Conference on Case-based Reasoning, Springer, 2004.
9. A. Aamodt and E. Plaza, Case-based reasoning: foundational issues, methodological variations, and system approaches, AICOM 7(1) (1994), 39-59.
10. J. Freedman and J. Tukey, A projection pursuit algorithm for exploratory data analysis, IEEE Transaction on Computers 23 (1974), 881-890.
11. C. Fyfe and E.S. Corchado, Maximum Likelihood Hebbian Rules, in: European Symposium on Artificial Neural Networks, 2002.
12. A.J. Smola and B. Scholkopf, A tutorial on support vector regression, Technical Report NC2-TR-1998-030, NeuroCOLT2 Technical Report Series, 1998.
13. 2 and nutrients in the high-latitude surface oceans: a comparative study, Global biochemical Cycles 7(4) (1993), 843-878.
14. 2 in the coastal upwelling off Chile: Spatial and temporal extrapolation using satellite data, Journal of Geophysical Research 107(0) (2002).
15. C. Fyfe, Negative feedback as an organising principle for artificial neural networks, PhD Thesis, Strathclyde University, 1995.
16. E. Corchado, D. MacDonald and C. Fyfe, Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit, Data Mining and Knowledge Discovery 8(3) (2004), 203-225.
17. H.S. Seung, N.D. Socci and D. Lee, The Rectified Gaussian Distribution, Advances in Neural Information Processing Systems 10 (1998).
18. E. Corchado and C. Fyfe, Orientation selection using Maximum Likelihood Hebbian Learning, International Journal of Knowledge-Based Intelligent Engineering Systems 7(2) (2003).
19. D. Charles, Unsupervised artificial neural networks for the identification of multiple causes in data, PhD thesis, University of Paisley, 1999.
20. E. Oja, Neural Networks, Principal components and subspaces, International Journal of Neural Systems 1 (1989), 61-68.
21. D. Charles and C. Fyfe, Modeling multiple cause structure using rectification constraints, Network: Computation in Neural Systems 9 (1998), 167-182.
22. L. Xu, Least mean square error reconstruction for self-organizing nets, Neural Networks 6 (1993), 627-648.
23. C. Fyfe and D. MacDonald, e-Insensitive Hebbian learning, Neuro Computing (2002).
24. J. Karhunen and J. Joutsensalo. Representation and separation of signals using non-linear PCA type learning, Neural Networks 7 (1994), 113-127.
25. A. Hyvärinen, Complexity pursuit: Separating interesting components from time series, Neural Computation 13 (2001), 883-898.
26. D.P. Bertsekas, Nonlinear Programming, Athena Scientific, Belmont, MA, 1995.
27. J. Bajo and J.M. Corchado, Evaluation and monitoring of the air-sea interaction using a CBR-agents approach, in: ICCBR 6th International Conference on Case-based Reasoning, Springer, 2005.
28. D. MacDonald, E. Corchado and C. Fyfe, Analysing spectroscopic data using Hierarchical Cooperative Maximum Likelihood Hebbian Learning, in: Mexican International Conference on Artificial Intelligence, Springer, 2004.