Summary
The purpose of this chapter is to introduce the reader to a type of artificial neural network called a multi-layer perceptron. The intention is not to present a detailed, comprehensive treatise on the subject; instead, we provide a brief, informal, tutorial in the spirit of Chapter 1.
We start with the historical background and then introduce the concept of regression. This concept holds for both continuous-valued and binary-valued response variables; however, when applied to the latter, probabilistic classification models are created.
In the context of medicine, probabilistic classification models are usually obtained using logistic regression analysis, but if logistic functions are nested to produce multi-layer perceptrons, the high flexibility of the resulting models enables them to handle complex classification tasks.
We discuss some important points that should be kept in mind when applying multi-layer perceptrons to classification problems. Finally, we end the tutorial with some recommended reading.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
I. Aleksander and H. Morton. An Introduction to Neural Computing. Chapman & Hall, London, 1990.
E.B. Baum and D. Haussler. What size net gives valid generalization? Neural Computation, 1(1):151–215, 1989.
R. Beale and T. Jackson. Neural Computing: An Introduction. IOP Publishing, Bristol, 1990.
C.M. Bishop. Novelty detection and neural network validation. IEE Proceedings: Vision, Image & Signal Processing, 141:217–222, 1994.
C.M. Bishop. Neural Networks for Pattern Recognition. Clarendon Press, Oxford, 1995.
A. Blum and P. Langley. Selection of relevant features and examples in machine learning. Artificial Intelligence, 97(1–2):245–271, 1997.
S.E. Fahlman and C. Lebiere. The cascade-correlation learning architecture. In D.S. Touretzky, editor, Advances in Neural Information Processing Systems 2, pages 524–532, Los Altos, CA, 1990. Morgan Kaufmann.
K. Gurney. An Introduction to Neural Networks. UCL Press, London, 1997.
D.J. Hand. Construction and Assessment of Classification Rules. John Wiley, Chichester, 1997.
D. Hebb. Organization of Behaviour. Wiley, New York, 1949.
K. Hornik. Approximation capabilities of multilayer feedforward networks. Neural Networks, 4(2):251–257, 1991.
R. Kohavi and G. John. Wrappers for feature selection. Artificial Intelligence, 97(1–2):273–324, 1997.
W.J. Krzanowski. Principles of Multivariate Analysis. Oxford University Press, Oxford, 1988.
D.J.C. MacKay. A practical Bayesian framework for back-propagation networks. Neural Computation, 4(3):448–472, 1992.
T. Masters. Practical Neural Network Recipes in C++. Academic Press, London, 1993.
W. McCulloch and W. Pitts. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5:115–133, 1943.
M.L. Minsky and S.A. Papert. Perceptrons. MIT Press, Cambridge, 1969.
I.T. Nabney. NETLAB: Algorithms for Pattern Recognition. Springer, London, 2002.
R.M. Neal. Bayesian Learning for Neural Networks. PhD thesis, University of Toronto, 1994.
N. Rashevsky. Topology and life: In search of general mathematical principles in biology and sociology. Bulletin of Mathematical Biophysics, 16:317–348, 1954.
B.D. Ripley. Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge, 1996.
F. Rosenblatt. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65:386–408, 1958.
F. Rosenblatt. On the convergence of reinforcement procedures in simple perceptrons. Technical report VG-1196-G-4, Cornell Aeronautical Laboratory, Buffalo, NY, 1960.
L. Tarassenko. A Guide to Neural Computing Applications. Arnold, London, 1998.
L. Tarassenko, P. Hayton, N. Cerneaz, and M. Brady. Novelty detection for the identification of masses in mammograms. In Proceedings of the 4th IEE International Conference on Artificial Neural Networks, pages 442–447, Cambridge, 1995. Cambridge University Press.
P.J. Werbos. Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. PhD thesis, Harvard University, Cambridge, MA, 1974.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag London Limited
About this chapter
Cite this chapter
Dybowski, R. (2005). A Casual View of Multi-Layer Perceptrons as Probability Models. In: Husmeier, D., Dybowski, R., Roberts, S. (eds) Probabilistic Modeling in Bioinformatics and Medical Informatics. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/1-84628-119-9_3
Download citation
DOI: https://doi.org/10.1007/1-84628-119-9_3
Publisher Name: Springer, London
Print ISBN: 978-1-85233-778-0
Online ISBN: 978-1-84628-119-8
eBook Packages: Computer ScienceComputer Science (R0)