Efficacy of modified backpropagation and optimisation methods on a real world medical problem

Alpsan, Dogan, Towsey, Michael W., Ozdamar, Ozcan, Tsoi, Ah Chung, & Ghista, Dhanjoo N. (1995) Efficacy of modified backpropagation and optimisation methods on a real world medical problem. Neural Networks, 8(6), pp. 945-962.

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A wide range of modifications to the backpropagation (BP) algorithm, motivated by heuristic arguments and optimisation theory, has been examined on a real-world medical signal classification problem. The method of choice depends both upon the nature of the learning task and whether one wants to optimise learning for speed or generalisation. It was found that, comparitively, standard BP was sufficiently fast and provided good generalisation when the task was to learn the training set within a given error tolerance. However, if the task was to find the global minimum, then standard BP failed to do so within 100,000 iterations, but first order methods which adapt the stepsize were as fast as, if not faster than, conjugate gradient and quasi-Newton methods. Second order methods required the same amount of fine tuning of line search and restart parameters as did the first order methods of their parameters in order to achieve optimum performance.

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ID Code: 7604
Item Type: Journal Article
Refereed: Yes
Additional Information: For more information, please refer to the journal's website (see hypertext link) or contact the author.
Keywords: neural networks, multilayer perceptron, backpropagation, optimisation, auditory evoked potential, pattern classification
DOI: 10.1016/0893-6080(95)00034-W
ISSN: 0893-6080
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Neural Evolutionary and Fuzzy Computation (080108)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 1995 Elsevier
Deposited On: 15 May 2007 00:00
Last Modified: 15 Jan 2009 07:32

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