6533b857fe1ef96bd12b435e
RESEARCH PRODUCT
Classical Training Methods
José D. MartínPaulo J. G. LisboaEmilio Soriasubject
Artificial neural networkComputer sciencebusiness.industrymedia_common.quotation_subjectTraining methodsMachine learningcomputer.software_genreError functionDelta ruleMultilayer perceptronArtificial intelligenceNonlinear classificationbusinessFunction (engineering)computermedia_commondescription
This chapter reviews classical training methods for multilayer neural networks. These methods are widely used for classification and function modelling tasks. Nevertheless, they show a number of flaws or drawbacks that should be addressed in the development of such systems. They work by searching the minimum of an error function which defines the optimal behaviour of the neural network. Different standard problems are used to show the capabilities of these models; in particular, we have benchmarked the algorithms in a nonlinear classification problem and in three function modelling problems.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2006-08-25 |