Applications of Autonomous Learning Multi-model Systems

Angelov, P.P. and Gu, X. (2019) Applications of Autonomous Learning Multi-model Systems. In: Empirical Approach to Machine Learning. Studies in Computational Intelligence, 800 . Springer-Verlag, pp. 277-293. ISBN 9783030023836

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Abstract

In this chapter, the algorithm summaries of the autonomous learning multi-model systems of zero-order (ALMMo-0) and first-order (ALMMo-1) described in Chap. 8 are provided. Numerical examples based on well-known benchmark datasets are presented for evaluating the classification performance of the ALMMo-0 and ALMMo-1 systems. Real-world problems are also used for evaluating the performance of the ALMMo-1 system on regression. Numerical experiments and the comparison with the state-of-the-art approaches demonstrate that ALMMo systems can produce highly accurate classification and regression results on various problems after a very efficient training process. Furthermore, ALMMo systems can learn from streaming data on a sample-by-sample basis, self-evolve its system structure and self-update the meta-parameters continuously with newly observed data, which makes the ALMMo system a very attractive solution for various real world applications. The pseudo-code of the main procedure of the ALMMo-0 system and the MATLAB implementation are provided in appendices B.3 and C.3, and the corresponding pseudo-code and MATLAB implementation of ALMMo-1 systems are provided in appendices B.4 and C.4, respectively. © 2019, Springer Nature Switzerland AG.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
129543
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Deposited On:
08 Jan 2019 14:45
Refereed?:
Yes
Published?:
Published
Last Modified:
30 Mar 2020 10:00