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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Gaussian Mixture Descriptors Learner

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Autor(es):
Freitas, Breno L. [1, 2] ; Silva, Renato M. [1] ; Almeida, Tiago A. [1]
Número total de Autores: 3
Afiliação do(s) autor(es):
[1] Fed Univ Sao Carlos UFSCar, Dept Comp Sci, Sorocaba, SP - Brazil
[2] Shopify Inc, Ottawa, ON - Canada
Número total de Afiliações: 2
Tipo de documento: Artigo Científico
Fonte: KNOWLEDGE-BASED SYSTEMS; v. 188, JAN 5 2020.
Citações Web of Science: 0
Resumo

In recent decades, various machine learning methods have been proposed to address classification problems. However, most of them do not support incremental (or online) learning and therefore are neither scalable nor robust to dynamic problems that change over time. In this study, a classification method was introduced based on the minimum description length principle, which offered a very good trade-off between model complexity and predictive power. The proposed method is lightweight, multiclass, and online. Moreover, despite its probabilistic nature, it can handle continuous features. Experiments conducted on real-world datasets with different characteristics demonstrated that the proposed method outperforms established online classification methods and is robust to overfitting, which is a desired characteristic for large, dynamic, and real-world classification problems. (C) 2019 Elsevier B.V. All rights reserved. (AU)

Processo FAPESP: 18/02146-6 - Representação distribuída de textos com atualização incremental
Beneficiário:Renato Moraes Silva
Linha de fomento: Bolsas no Brasil - Pós-Doutorado
Processo FAPESP: 17/09387-6 - Modelo de representação distribuída de textos com capacidade de evoluir continuamente
Beneficiário:Tiago Agostinho de Almeida
Linha de fomento: Auxílio à Pesquisa - Regular