Co-training study for Online Regression

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Ricardo Sousa (Author) Other; João Gama (Author) FEP

Conference proceedings International

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

Pages: 529-531

33rd Annual ACM Symposium on Applied Computing, SAC 2018

9 April 2018 through 13 April 2018

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 3