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Applications of Autonomous Learning Multi-model Systems

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

Publication date2019
Host publicationEmpirical Approach to Machine Learning
EditorsPlamen Angelov, Xiaowei Gu
Number of pages17
ISBN (print)9783030023836
<mark>Original language</mark>English

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X


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.