Multilayer perceptrons

Research output: Chapter in Book/Report/Conference proceedingEntry for encyclopedia/dictionary

Abstract

This article briefly introduces Artificial Neural Networks for beginners. In its first part, it describes the functioning of a single artificial neuron for simple binary classification tasks. The method is later generalized to single-layer networks, whose functioning is shown on simple multi-class classification tasks. Than, the fact that single-layer Neural Networks may fail to correctly classify non-linearly separable problems is shown by means of a simple example. Finally, multi-layer Neural Networks are presented, as more general methods, able to obtain satisfactory results potentially on any kind of classification problem, including non-linearly separable ones.

Original languageEnglish
Title of host publicationEncyclopedia of Bioinformatics and Computational Biology
Subtitle of host publicationABC of Bioinformatics
EditorsShoba Ranganathan, Michael Gribskov, Kenta Nakai, Christian Schönbach
PublisherElsevier
Pages612-620
Number of pages9
Volume1-3
ISBN (Electronic)9780128114322
ISBN (Print)9780128114148
DOIs
Publication statusPublished - 2019

Keywords

  • Artificial neural networks
  • Multi-layer neural networks
  • Non-linearly separable problems
  • Perceptron
  • Perceptron learning rule

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  • Cite this

    Vanneschi, L., & Castelli, M. (2019). Multilayer perceptrons. In S. Ranganathan, M. Gribskov, K. Nakai, & C. Schönbach (Eds.), Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics (Vol. 1-3, pp. 612-620). Elsevier. https://doi.org/10.1016/B978-0-12-809633-8.20339-7