Multiple classifier systems: Software engineered, automatically modular leading to a taxonomic overview

Pattern Analysis and Applications

Volumn 5, Issue 2, 2002, Pages 180-188

  Partridge, Derek ^a   Griffith, Niall ^b  

^a UNIVERSITY OF EXETER   (United Kingdom)

^b UNIVERSITY OF LIMERICK   (Ireland)

Author keywords

Classifier diversity; Classifier independence; MCS taxonomy; Modular subnets

Indexed keywords

EID: 0036086170     PISSN: 14337541     EISSN: None     Source Type: Journal    
DOI: 10.1007/s100440200016     Document Type: Article

References (23)

1. Knight JC, Leveson NG. An experimental evaluation of the assumption of independence in multiversion programming. IEEE Trans Software Engineering 1986; 12:96-109
2. Eckhardt DE, Lee LD. A theoretical basis for the analysis of multiversion software subject to coincident errors. IEEE Trans Software Engineering 1985; 11(12):1511-1517
3. Littlewood B, Miller DR. Conceptual modelling of coincident failures in multiversion software. IEEE Trans Software Engineering 1989; 15(12):1596-1614
4. Partridge D, Krzanowski WJ. Software diversity: practical statistics for its measurement and exploitation. Information & Software Technology 1997; 39:707-717
5. Adams JM, Taha A. An experiment in software redundancy with diverse methodologies. Proceedings 25th Hawaii Intrenat Conf on System Sciences 1992; 83-90
6. Aviziensis A, Lyu MR, Schutz W. In search of effective diversity: a six-language study of fault-tolerant flight-control software. Digest 18th Ann Internat Symp Fault Tolerant Computing 1988; 15-22
7. Bishop PG, Esp DG, Barnes M, Humphreys P, Dahll G, Lahti J. PODS - A project on diverse software. IEEE Trans Software Engineering. 1986; 12(9):929-940
8. Partridge D, Krzanowski WJ. Distinct-failure diversity and multi-version software. Res. Rep. No. 348, Department of Computer Science, University of Exter, 1999
9. Bishop CM. Neural Networks for Pattern Recognition. Clarendon Press, Oxford, 1995
10. Schapire R. The strength of weak learnability. Machine Learning 1990; 5(2):197-227
11. Drucker H, Schapire R, Simard P. Improving performance in neural networks using a boosting algorithm. Advances in Neural Information Processing Systems 1993; 5:42-49
12. Perrone M (ed). Pulling it all together: methods for combining neural networks. ONR Tech. Rep. 69, Institute for Brain and Neural Systems, Brown University (mpp@brown.edu) 1993
13. Bauer E, Kohavi R. An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Machine Learning 1998; 13:1-38
14. Jacobs RA, Jordan MI, Barto AG. Task decomposition through competition in a modular connectionist architecture: the what and where vision tasks. Cognitive Science 1991; 15:219-250
15. Jacobs RA, Jordan MI, Nowlan SJ, Hinton GE. Adaptive mixtures of local experts. Neural Computation 1991; 3:79-87
16. Jacobs RA, Jordan MI. A competitive modular connectionist architecture. In: RP Lippman, J Moody, DS Touretzky (eds), Advances in Neural Information Processing Systems 3, 1991; 767-773
17. Nowlan SJ, Hinton GE. Evaluation of adaptive mixtures of competing experts. In: RP Lippman, J Moody, DS Touretzky (eds), Advances in Neural Information Processing Systems 3, 1991; 774-780
18. Jordan MI, Jacobs RA. Hierarchical mixtures of experts and the EM algorithm. Neural Computation 1994; 6:181-214
19. Jacobs RA. Bias/Variance analyses of mixtures-of-experts architectures. Neural Computation 1997; 9:369-383
20. Kohonen T. Self-Organization and Associative Memory. Springer-Verlag, Berlin, 1989
21. Frey P, Slate D. Letter recognition using holland-style adaptive classifiers. Machine Learning 1991; 6:161-182
22. Rumelhart DE, Hinton GE, Williams RJ. Learning internal representations by error propagation. In: DE Rumelhart, JL McClelland (eds), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1: Foundations. MIT Press, Cambridge, MA, 1986
23. Partridge D, Yates WB. Engineering multiversion neural-net systems. Neural Computation 1996; 8(4):869-S93