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Applications of Deep Rule-Based Classifiers

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Applications of 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. 295-319 (Studies in Computational Intelligence; Vol. 800).

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

Harvard

Angelov, PP & Gu, X 2019, Applications of 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. 295-319. https://doi.org/10.1007/978-3-030-02384-3_13

APA

Angelov, P. P., & Gu, X. (2019). Applications of Deep Rule-Based Classifiers. In P. Angelov, & X. Gu (Eds.), Empirical Approach to Machine Learning (Vol. 800, pp. 295-319). (Studies in Computational Intelligence; Vol. 800). Springer-Verlag. https://doi.org/10.1007/978-3-030-02384-3_13

Vancouver

Angelov PP, Gu X. Applications of Deep Rule-Based Classifiers. In Angelov P, Gu X, editors, Empirical Approach to Machine Learning. Vol. 800. Springer-Verlag. 2019. p. 295-319. (Studies in Computational Intelligence). Epub 2018 Oct 18. doi: 10.1007/978-3-030-02384-3_13

Author

Angelov, P.P. ; Gu, X. / Applications of Deep Rule-Based Classifiers. Empirical Approach to Machine Learning. editor / Plamen Angelov ; Xiaowei Gu. Vol. 800 Springer-Verlag, 2019. pp. 295-319 (Studies in Computational Intelligence).

Bibtex

@inbook{dbe11a1511f84f28a2ae3682d9089f2c,
title = "Applications of Deep Rule-Based Classifiers",
abstract = "In this chapter, the algorithm summary of the main procedure of the deep rule-based (DRB) classifier described in Chap. 9 is provided. Numerical examples based on popular benchmark image sets including, handwritten digits recognition, remote sensing scene classification, face recognition and object recognition, etc., are presented for evaluating the performance of the DRB algorithm on image classification, and the state-of-the-art approaches are used for comparison. Numerical experiments show that DRB classifier is able to perform highly accurate classification in various image classification problems, and also demonstrate the advantages of its prototype-based nature and transparency over the existing approaches. The pseudo-code of the main procedure of the DRB classifier and the MATLAB implementations can be found in appendices B.5 and C.5, respectively. {\textcopyright} 2019, Springer Nature Switzerland AG.",
author = "P.P. Angelov and X. Gu",
year = "2019",
doi = "10.1007/978-3-030-02384-3_13",
language = "English",
isbn = "9783030023836",
volume = "800",
series = "Studies in Computational Intelligence",
publisher = "Springer-Verlag",
pages = "295--319",
editor = "Angelov, {Plamen } and Gu, {Xiaowei }",
booktitle = "Empirical Approach to Machine Learning",

}

RIS

TY - CHAP

T1 - Applications of 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 deep rule-based (DRB) classifier described in Chap. 9 is provided. Numerical examples based on popular benchmark image sets including, handwritten digits recognition, remote sensing scene classification, face recognition and object recognition, etc., are presented for evaluating the performance of the DRB algorithm on image classification, and the state-of-the-art approaches are used for comparison. Numerical experiments show that DRB classifier is able to perform highly accurate classification in various image classification problems, and also demonstrate the advantages of its prototype-based nature and transparency over the existing approaches. The pseudo-code of the main procedure of the DRB classifier and the MATLAB implementations can be found in appendices B.5 and C.5, respectively. © 2019, Springer Nature Switzerland AG.

AB - In this chapter, the algorithm summary of the main procedure of the deep rule-based (DRB) classifier described in Chap. 9 is provided. Numerical examples based on popular benchmark image sets including, handwritten digits recognition, remote sensing scene classification, face recognition and object recognition, etc., are presented for evaluating the performance of the DRB algorithm on image classification, and the state-of-the-art approaches are used for comparison. Numerical experiments show that DRB classifier is able to perform highly accurate classification in various image classification problems, and also demonstrate the advantages of its prototype-based nature and transparency over the existing approaches. The pseudo-code of the main procedure of the DRB classifier and the MATLAB implementations can be found in appendices B.5 and C.5, respectively. © 2019, Springer Nature Switzerland AG.

U2 - 10.1007/978-3-030-02384-3_13

DO - 10.1007/978-3-030-02384-3_13

M3 - Chapter (peer-reviewed)

SN - 9783030023836

VL - 800

T3 - Studies in Computational Intelligence

SP - 295

EP - 319

BT - Empirical Approach to Machine Learning

A2 - Angelov, Plamen

A2 - Gu, Xiaowei

PB - Springer-Verlag

ER -