Co-training study for Online Regression

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2018

Autores

Ricardo Sousa (Autor) Outra; João Gama (Autor) FEP

Ata de Conferência Internacional

Título: Proceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC 2018, Pau, France, April 09-13, 2018

Páginas: 529-531

33rd Annual ACM Symposium on Applied Computing, SAC 2018

9 April 2018 through 13 April 2018

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00N-ZRA

DOI: 10.1145/3167132.3174909

Abstract (EN): This paper describes the development of a Co-training (semi-supervised approach) method that uses multiple learners for single target regression on data streams. The experimental evaluation was focused on the comparison between a realistic supervised scenario (all unlabelled examples are discarded) and scenarios where unlabelled examples are used to improve the regression model. Results present fair evidences of error measure reduction by using the proposed Co-training method. However, the error reduction still is relatively small.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 3