Multilayer perceptrons

Leonardo Vanneschi; Mauro Castelli

doi:10.1016/B978-0-12-809633-8.20339-7

Multilayer perceptrons

Leonardo Vanneschi, Mauro Castelli

Research output: Chapter in Book/Report/Conference proceeding › Entry for encyclopedia/dictionary › peer-review

26 Citations (Scopus)

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 language	English
Title of host publication	Encyclopedia of Bioinformatics and Computational Biology
Subtitle of host publication	ABC of Bioinformatics
Editors	Shoba Ranganathan, Michael Gribskov, Kenta Nakai, Christian Schönbach
Publisher	Elsevier
Pages	612-620
Number of pages	9
Volume	1-3
ISBN (Electronic)	9780128114322
ISBN (Print)	9780128114148
DOIs	https://doi.org/10.1016/B978-0-12-809633-8.20339-7
Publication status	Published - 2019

Keywords

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

Access to Document

10.1016/B978-0-12-809633-8.20339-7

Cite this

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title = "Multilayer perceptrons",

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.",

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author = "Leonardo Vanneschi and Mauro Castelli",

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Multilayer perceptrons. / Vanneschi, Leonardo ; Castelli, Mauro.
Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics. ed. / Shoba Ranganathan; Michael Gribskov; Kenta Nakai; Christian Schönbach. Vol. 1-3 Elsevier, 2019. p. 612-620.

Research output: Chapter in Book/Report/Conference proceeding › Entry for encyclopedia/dictionary › peer-review

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AB - 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.

KW - Artificial neural networks

KW - Multi-layer neural networks

KW - Non-linearly separable problems

KW - Perceptron

KW - Perceptron learning rule

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A2 - Gribskov, Michael

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A2 - Schönbach, Christian

PB - Elsevier

ER -

Multilayer perceptrons

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