A new perspective for Minimal Learning Machines: A lightweight approach-Reference-Cited by-同舟云学术

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A new perspective for Minimal Learning Machines: A lightweight approach

Published:2020-08 Issue: Volume:401 Page:308-319
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Florêncio José A.V.,Oliveira Saulo A.F.^ORCID,Gomes João P.P.^ORCID,Neto Ajalmar R. Rocha^ORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference24 articles.

1. MLM-rank: a ranking algorithm based on the minimal learning machine;Alencar,2015

2. Fast co-MLM: an efficient semi-supervised co-training method based on the minimal learning machine;Caldas;New Gener. Comput.,2018

3. Regional models and minimal learning machines for nonlinear dynamic system identification;de Souza Júnior,2014

4. SEP 21 2015. Minimal Learning Machine: a novel supervised distance-based approach for regression and classification. NEUROCOMPUTING 164, 34–44;de Souza Junior,2013

5. Statistical comparisons of classifiers over multiple data sets;Demšar;J. Mach. Learn. Res.,2006

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Statistical Similarity in Machine Learning;Machine Learning Applications;2023-12-11

2. Do Randomized Algorithms Improve the Efficiency of Minimal Learning Machine?;Machine Learning and Knowledge Extraction;2020-11-13

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