Electrostatic field framework for supervised and semi-supervised learning from incomplete data

Natural Computing

Volumn 10, Issue 2, 2011, Pages 921-945

  Budka, Marcin ^a   Gabrys, Bogdan ^a  

^a BOURNEMOUTH UNIVERSITY   (United Kingdom)

Author keywords

Deficient data; Electrostatic field; Gravity field; Hybrid learning; Incomplete data; Machine learning; Missing data; Pattern classification; Physical phenomena

Indexed keywords

CLASSIFIER; CONFERENCE PAPER; INFORMATION PROCESSING; INFORMATION SCIENCE; INTERMETHOD COMPARISON; LEARNING ALGORITHM; MACHINE LEARNING; MATHEMATICAL MODEL; QUALITY CONTROL;

EID: 79958064103     PISSN: 15677818     EISSN: 15729796     Source Type: Journal    
DOI: 10.1007/s11047-010-9182-4     Document Type: Conference Paper

References (45)

1. C Aggarwal 2001 Re-designing distance functions and distance-based applications for high dimensional data ACM SIGMOD Rec 30 1 13 18 10.1145/373626.373638 (Pubitemid 33604419)
2. C Aggarwal A Hinneburg D Keim 2001 On the surprising behavior of distance metrics in high dimensional space Lect Notes Comput Sci 2001 420 435 10.1007/3-540-44503-X-27
3. Asuncion A, Newman D (2007) UCI machine learning repository
4. K Beyer J Goldstein R Ramakrishnan U Shaft 1999 When is "nearest neighbor" meaningful Lect Notes Comput Sci 1540 217 235 10.1007/3-540-49257-7-15
5. Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Proceedings of the eleventh annual conference on computational learning theory. ACM, New York, NY, USA, pp 92-100
6. Budka M, Gabrys B (2009) Electrostatic field classifier for deficient data. In: Computer recognition systems 3: Proceedings of 6th international conference on computer recognition systems cores 09. Springer, pp 311-318
7. Chuang I, Nielsen M (2000) Quantum information. Cambridge University Press
8. Dara R, Kremer S, Stacey D (2002) Clustering unlabeled data with SOMs improves classification of labeled real-world data. In: Neural networks, 2002. IJCNN'02. Proceedings of the 2002 international joint conference on, vol 3, pp 2237-2242 (Pubitemid 34647979)
9. A Dempster N Laird D Rubin 1977 Maximum likelihood from incomplete data via the EM algorithm J R Stat Soc Ser B (Methodol) 39 1 1 38 501537 0364.62022
10. Duin R, Juszczak P, Paclik P, Pekalska E, de Ridder D, Tax D, Verzakov S (2007) Pr-tools 4.1, a matlab toolbox for pattern recognition. http://prtools.org
11. Francois D, Wertz V, Verleysen M (2005) Non-Euclidean metrics for similarity search in noisy datasets. In: Proceedings of the European symposium on artificial neural networks, pp 339-334
12. B Gabrys 2002 Neuro-fuzzy approach to processing inputs with missing values in pattern recognition problems Int J Approx Reason 30 3 149 179 1937312 1033.68093 10.1016/S0888-613X(02)00070-1
13. B Gabrys L Petrakieva 2004 Combining labelled and unlabelled data in the design of pattern classification systems Int J Approx Reason 35 3 251 273 2058426 1068.68681 10.1016/j.ijar.2003.08.005
14. Z Ghahramani M Jordan J Cowan G Tesauro J Alspector 1994 Supervised learning from incomplete data via an EM approach Adv Neural Inf Process Syst 6 120 127
15. J Graham P Cumsille E Elek-Fisk 2003 Methods for handling missing data Handb Psychol 2 87 114
16. Hakkoymaz H, Chatzimilioudis G, Gunopulos D, Mannila H (2009) Applying electromagnetic field theory concepts to clustering with constraints. In: Proceedings of the European conference on machine learning and knowledge discovery in databases: part I. Springer, p 500
17. K Hild D Erdogmus J Principe 2001 Blind source separation using Renyi's mutual information IEEE Signal Process Lett 8 6 174 176 10.1109/97.923043 (Pubitemid 32498951)
18. Hochreiter S, Mozer M (2001) Coulomb classifiers: reinterpreting SVMs as electrostatic systems. Technical report CU-CS-921-01. Department of Computer Science, University of Colorado, Boulder
19. S Hochreiter M Mozer K Obermayer 2003 Coulomb classifiers: generalizing support vector machines via an analogy to electrostatic systems Adv Neural Inf Process Syst 15 545 552
20. R Jenssen T Eltoft D Erdogmus J Principe 2006 Some equivalences between kernel methods and information theoretic methods J VLSI Signal Process 45 1 49 65 10.1007/s11265-006-9771-8 (Pubitemid 46132343)
21. Kothari R, Jain V (2002) Learning from labeled and unlabeled data. In: Neural networks, 2002. IJCNN'02. Proceedings of the 2002 international joint conference on, vol 3
22. Kuncheva L (2000) Fuzzy classifier design. Physica Verlag
23. D Loss D DiVincenzo 1998 Quantum computation with quantum dots Phys Rev A 57 1 120 126 10.1103/PhysRevA.57.120 (Pubitemid 128613300)
24. Madow W, Olkin I (1983) Incomplete data in sample surveys, vol 3, Proceedings of the symposium. Academic Press, New York
25. Mitchell T (1999) The role of unlabeled data in supervised learning. In: Proceedings of the sixth international colloquium on cognitive science
26. Nigam K, Ghani R (2000) Understanding the behavior of co-training. In: Proceedings of KDD-2000 workshop on text mining
27. Outhwaite W, Turner SP (2007) Handbook of social science methodology. SAGE Publications Ltd
28. W Pedrycz J Waletzky 1997 Fuzzy clustering with partial supervision IEEE Trans Syst Man Cybern B 27 5 787 795 10.1109/3477.623232 (Pubitemid 127770674)
29. Principe J, Xu D, Fisher J (2000a) Information theoretic learning, chapter 7. Wiley, New York, pp 265-319
30. J Principe D Xu Q Zhao J Fisher 2000 Learning from examples with information theoretic criteria J VLSI Signal Process 26 1 61 77 0965.68135 10.1023/A:1008143417156
31. Ripley B (1996) Pattern recognition and neural networks. Cambridge University Press
32. Roy N, McCallum A (2001) Toward optimal active learning through sampling estimation of error reduction. In: Proceedings of the 18th international conference on machine learning, pp 441-448
33. D Rubin 1976 Inference and missing data Biometrika 63 3 581 592 455196 0344.62034 10.1093/biomet/63.3.581
34. Rubin D (1987) Multiple imputation for nonresponse in surveys. Wiley-Interscience
35. D Ruta B Gabrys 2003 Physical field models for pattern classification Soft Comput 8 2 126 141 2505750
36. Ruta D, Gabrys B (2005) Nature inspired learning models. In: Proceedings of the European symposium on nature inspired smart information systems, Albufeira, Portugal
37. D Ruta B Gabrys 2009 A framework for machine learning based on dynamic physical fields Nat Comput 8 2 219 237 2505750 1188.68236 10.1007/s11047-007- 9064-6
38. W Sarle 1998 Prediction with missing inputs JCIS 98 399 402
39. J Schafer J Graham 2002 Missing data: our view of the state of the art Psychol Methods 7 2 147 177 10.1037/1082-989X.7.2.147
40. J Schafer N Schenker 2000 Inference with imputed conditional means J Am Stat Assoc 95 449 144 154 1803147 1014.62023 10.2307/2669534
41. Sg SG, Goldman S, Zhou Y (2000) Enhancing supervised learning with unlabeled data. Proceedings of the 17th international conference on machine learning, pp 327-334
42. K Torkkola 2003 Feature extraction by non parametric mutual information maximization J Mach Learn Res 3 1415 1438 2020765 1102.68638 10.1162/153244303322753742
43. V Tresp S Ahmad R Neuneier 1994 Training neural networks with deficient data Adv Neural Inf Process Syst 6 128 135
44. P Walther K Resch T Rudolph E Schenck H Weinfurter V Vedral M Aspelmeyer A Zeilinger 2005 Experimental one-way quantum computing Nature 434 169 176 10.1038/nature03347 (Pubitemid 40388041)
45. Zurek W (1989) Complexity, entropy and the physics of information. Westview Press