An Advanced Pruning Method in the Architecture of Extreme Learning Machines Using L1-Regularization and Bootstrapping

Autor: Souza, Paulo Vitor de Campos, Torres, Luiz Carlos Bambirra, Silva, Gustavo Rodrigues Lacerda, Braga, Antonio de Padua, Lughofer, Edwin

Publikováno v: Electronics, Vol 9, Iss 811, p 811 (2020)
Electronics
Volume 9
Issue 5

Extreme learning machines (ELMs) are efficient for classification, regression, and time series prediction, as well as being a clear solution to backpropagation structures to determine values in intermediate layers of the learning model. One of the pr

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https://www.mdpi.com/2079-9292/9/5/811

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Pulsar Detection for Wavelets SODA and Regularized Fuzzy Neural Networks Based on Andneuron and Robust Activation Function.

Autor: de Campos Souza, Paulo Vitor^1,2 goldenpaul@informatica.esp.ufmg.br, Torres, Luiz Carlos Bambirra³ luizlitc@gmail.com, Guimarães, Augusto Junio⁴ augustojunioguimaraes@gmail.com, Araujo, Vanessa Souza⁴ v.souzaaraujo@yahoo.com.br

Publikováno v: International Journal on Artificial Intelligence Tools. Feb2019, Vol. 28 Issue 1, pN.PAG-N.PAG. 25p.

A maximum margin-based kernel width estimator and its application to the response to neoadjuvant chemotherapy

Autor: Wanderley, Maria Fernanda Barbosa, Torres, Luiz Carlos Bambirra, Natowicz, René, Braga, Antônio Pádua

Publikováno v: Revista Brasileira de Engenharia Biomédica
Revista Brasileira de Engenharia Biomédica, 2014, 30 (1), pp.17-26
Revista Brasileira de Engenharia Biomédica, Volume: 30, Issue: 1, Pages: 17-26, Published: MAR 2014
Revista Brasileira de Engenharia Biomédica v.30 n.1 2014
Sociedade Brasileira de Engenharia Biomédica (SBEB)
instacron:SBEB

INTRODUCTION: Function induction problems are frequently represented by affinity measures between the elements of the inductive sample set, and kernel matrices are a well-known example of affinity measures. METHODS: The objective of the present work

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::005279e72a413bb367868176bdc34eb5
https://hal.archives-ouvertes.fr/hal-02001215