Minimal MLPs do not model the XOR logic [6]

Neurocomputing

Volumn 48, Issue 1-4, 2002, Pages 1033-1037

  Lisboa, P J G ^a   Etchells, T A ^a   Pountney, D C ^a  

^a LIVERPOOL JOHN MOORES UNIVERSITY   (United Kingdom)

Author keywords

Exclusive OR; Neural network design; Rule extraction

Indexed keywords

FORMAL LOGIC; FUNCTIONS; LOGIC GATES; PROBLEM SOLVING;

LOGIC FUNCTIONS;

MULTILAYER NEURAL NETWORKS;

ANALYTIC METHOD; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; DATA ANALYSIS; FACE; LETTER; LOGIC; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; PRIORITY JOURNAL; PROBLEM SOLVING;

EID: 0036825540     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0925-2312(02)00607-0     Document Type: Letter

References (7)

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2. D.W. Hosmer, S. Lemeshow, Applied Logistic Regression, Wiley, New York, 1989.
3. P.J.G. Lisboa, A review of evidence of health benefit from artificial neural networks in medical intervention, Neural Networks 15 (1) (2002) 9-37.
4. P.J.G. Lisboa, S.J. Perantonis, Complete solution of the local minima in the XOR problem, Network: Comput. Neural Systems 2 (1) (1991) 119-124.
5. I.G. Sprinkhuizen-Kuyper, E.J.W. Boers, The error surface of the simplest XOR network has only global minima, Neural Comput. 8 (6) (1996) 1301-1320.
6. H. Tsukimoto, Extracting rules from trained neural networks, IEEE Trans. Neural Networks 11 (2) (2000) 377-389.
7. C.K.I. Williams, C.E. Rasmussen, Gaussian processes for regression, in: D.R. Touretsky, M.C. Mozer, M.E. Hasselmo (Eds.), Advances in Neural Information Processing Systems, Vol. 8, MIT Press, Cambridge, MA, 1996, pp. 514-520.