Inductive Learning Algorithms for Complex Systems Modeling

By H. Madala, A. G. Ivakhnenko. 2017

250 citations recorded by the source corpus.

Inductive Learning Algorithms for complex Systems Modeling is a professional monograph that surveys new types of learning algorithms for modelling complex scientific systems in science and engineering. The book features discussions of algorithm development, structure, and behaviour; comprehensive coverage of all types of algorithms useful for this subject; and applications of various modelling activities (eg environmental systems, noise immunity, economic systems, clusterisation, and neural networks). It presents recent studies on clusterisation and recognition problems, and includes listings of algorithms in FORTRAN that can be run directly on IBM-compatible

Subfields: Artificial Intelligence

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Citations in the corpus

Deep learning in neural networks: An overview (2014, background): “The contents of this paper may be used for educational and noncommercial purposes, including articles for Wikipedia and similar sites.”
Managing computational complexity using surrogate models: a critical review (2020, background): “BTs are used in supervised learning problems where multiple features x i are available in a training dataset to estimate the response variable y i (Madala and Ivakhnenko 1994).”