Further Reading

Efron B and Tibshirani RJ (1995) Cross-validation and the bootstrap: Estimating the error rate of the prediction rule. Technical Report 176, Department of Statistics, University of Toronto. Toronto: University of Toronto.

Engelbrecht AP, Cloete I, and Zurada JM (1995) Determining the significance of input parameters using sensitivity analysis. From natural to artificial neural computation. Malaga-Torremolinos, Spain: Springer.

Geman S, Bienenstock E, and Doursat R (1992) Neural networks and the bias/variance dilemma. Neural Computation 4: 1-58.

Gevrey M, Dimopoulos I, and Lek S (2003) Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling 160: 249-264.

Gevrey M, Dimopoulos I, and LekS (2006) A two-way interaction of input variables in an artificial neural network model. Ecological Modelling 195: 43-50.

Hornik K, Stinchcombe M, and White H (1989) Multilayer feed forward neural networks are universal approximators. Neural Networks 2: 359-366.

Lek S, Delacoste M, Baran P, et al. (1996) Application of neural networks to modelling nonlinear relationships in ecology. Ecological Modelling 90: 39-52.

Parker DB (1982) Learning logic. Invention Report S81-64, File 1, Office of Technology Licensing, Stanford University. Stanford, CA: Stanford.

Rumelhart DE, Hinton GE, and Williams RJ (1986) Learning representations by backpropagation error. Nature 323: 533-536.

Webos P (1974) Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. PhD Thesis, Harvard University.

Zurada JM (1992) Introduction to Artificial Neural Systems. New York: West Publishing Company.

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