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.
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J. Gan, Q. Li, Z. Zhang, J. Wang, Two-level feature representation for aerial scene classification. IEEE Geosci. Remote Sens. Lett. 13(11), 1626–1630 (2016)
Y. Yang, S. Newsam, Bag-of-visual-words and spatial extensions for land-use classification, in International Conference on Advances in Geographic Information Systems (2010) pp. 270–279
L. Fei-Fei, R. Fergus, P. Perona, One-shot learning of object categories. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 594–611 (2006)
N. Cristianini, J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods (Cambridge University Press, Cambridge, 2000)
P. Cunningham, S.J. Delany, K-nearest neighbour classifiers. Mult. Classif. Syst. 34, 1–17 (2007)
D. Zhou, O. Bousquet, T. N. Lal, J. Weston, B. Schölkopf, Learning with local and global consistency. Adv. Neural. Inform. Process Syst., 321–328 (2004)
J. Wang, T. Jebara, S.F. Chang, Semi-supervised learning using greedy Max-Cut. J. Mach. Learn. Res. 14, 771–800 (2013)
M. Belkin, P. Niyogi, V. Sindhwani, Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 7(2006), 2399–2434 (2006)
L. Gómez-Chova, G. Camps-Valls, J. Munoz-Mari, J. Calpe, Semisupervised image classification with Laplacian support vector machines. IEEE Geosci. Remote Sens. Lett. 5(3), 336–340 (2008)
L. Fei-Fei, R. Fergus, P. Perona, Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. Comput. Vis. Image Underst. 106(1), 59–70 (2007)
X. Gu, P.P. Angelov, Semi-supervised deep rule-based approach for image classification. Appl. Soft Comput. 68, 53–68 (2018)
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Angelov, P.P., Gu, X. (2019). Applications of Semi-supervised Deep Rule-Based Classifiers. In: Empirical Approach to Machine Learning. Studies in Computational Intelligence, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-030-02384-3_14
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DOI: https://doi.org/10.1007/978-3-030-02384-3_14
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