Applications of Semi-supervised Deep Rule-Based Classifiers

Angelov, P.P. and Gu, X. (2019) Applications of Semi-supervised Deep Rule-Based Classifiers. In: Empirical Approach to Machine Learning. Studies in Computational Intelligence, 800 . Springer-Verlag, pp. 321-340. ISBN 9783030023836

Full text not available from this repository.

Abstract

In this chapter, the algorithm summary of the main procedure of the semi-supervised deep rule-based (SS_DRB) classifier described in Chap. 9 is provided, which serves as a powerful extension of the DRB classifier. The offline learning process of the SS_DRB classifier is illustrated and the performance of the SS_DRB algorithm is evaluated based on benchmark image sets. Numerical examples and comparison with the state-of-the-art semi-supervised learning approaches demonstrate that SS_DRB classifier can achieve highly accurate classification results with only a handful of labelled training images, and it consistently outperforms the alternative approaches. The pseudo-code of the main procedure of the SS_DRB classifier and the MATLAB implementations can be found in appendices B.6 and C.6, respectively. © 2019, Springer Nature Switzerland AG.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
129539
Deposited By:
Deposited On:
08 Jan 2019 15:00
Refereed?:
Yes
Published?:
Published
Last Modified:
30 Mar 2020 10:00