Home > Research > Publications & Outputs > Applications of Semi-supervised Deep Rule-Based...

Links

Text available via DOI:

View graph of relations

Applications of Semi-supervised Deep Rule-Based Classifiers

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter (peer-reviewed)peer-review

Published

Standard

Applications of Semi-supervised Deep Rule-Based Classifiers. / Angelov, P.P.; Gu, X.

Empirical Approach to Machine Learning. ed. / Plamen Angelov; Xiaowei Gu. Vol. 800 Springer-Verlag, 2019. p. 321-340 (Studies in Computational Intelligence; Vol. 800).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter (peer-reviewed)peer-review

Harvard

Angelov, PP & Gu, X 2019, Applications of Semi-supervised Deep Rule-Based Classifiers. in P Angelov & X Gu (eds), Empirical Approach to Machine Learning. vol. 800, Studies in Computational Intelligence, vol. 800, Springer-Verlag, pp. 321-340. https://doi.org/10.1007/978-3-030-02384-3_14

APA

Angelov, P. P., & Gu, X. (2019). Applications of Semi-supervised Deep Rule-Based Classifiers. In P. Angelov, & X. Gu (Eds.), Empirical Approach to Machine Learning (Vol. 800, pp. 321-340). (Studies in Computational Intelligence; Vol. 800). Springer-Verlag. https://doi.org/10.1007/978-3-030-02384-3_14

Vancouver

Angelov PP, Gu X. Applications of Semi-supervised Deep Rule-Based Classifiers. In Angelov P, Gu X, editors, Empirical Approach to Machine Learning. Vol. 800. Springer-Verlag. 2019. p. 321-340. (Studies in Computational Intelligence). https://doi.org/10.1007/978-3-030-02384-3_14

Author

Angelov, P.P. ; Gu, X. / Applications of Semi-supervised Deep Rule-Based Classifiers. Empirical Approach to Machine Learning. editor / Plamen Angelov ; Xiaowei Gu. Vol. 800 Springer-Verlag, 2019. pp. 321-340 (Studies in Computational Intelligence).

Bibtex

@inbook{dd8c16fa6fd541bebc854d8f5a49f531,
title = "Applications of Semi-supervised Deep Rule-Based Classifiers",
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. {\textcopyright} 2019, Springer Nature Switzerland AG.",
author = "P.P. Angelov and X. Gu",
year = "2019",
doi = "10.1007/978-3-030-02384-3_14",
language = "English",
isbn = "9783030023836",
volume = "800",
series = "Studies in Computational Intelligence",
publisher = "Springer-Verlag",
pages = "321--340",
editor = "Plamen Angelov and Xiaowei Gu",
booktitle = "Empirical Approach to Machine Learning",

}

RIS

TY - CHAP

T1 - Applications of Semi-supervised Deep Rule-Based Classifiers

AU - Angelov, P.P.

AU - Gu, X.

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-030-02384-3_14

DO - 10.1007/978-3-030-02384-3_14

M3 - Chapter (peer-reviewed)

SN - 9783030023836

VL - 800

T3 - Studies in Computational Intelligence

SP - 321

EP - 340

BT - Empirical Approach to Machine Learning

A2 - Angelov, Plamen

A2 - Gu, Xiaowei

PB - Springer-Verlag

ER -