Title Details: | |
Classic Computational Intelligence Extensions |
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Authors: |
Kampourlazos, Vasileios Papakostas, Georgios |
Reviewer: |
Kechagias, Athanasios |
Subject: | MATHEMATICS AND COMPUTER SCIENCE > COMPUTER SCIENCE > INTELLIGENT SYSTEMS |
Description: | |
Abstract: |
The aforementioned methodologies of classical Computational Intelligence are extended through synergies among them and/or adopting alternative computational techniques such as machine learning techniques, etc. Specifically, combinations of neural networks and fuzzy system models are proposed, which can be optimized using evolutionary computation. In this context, decision trees based on rules are presented as an extension of fuzzy system rules. Following a similar approach, radial basis function networks are introduced as an alternative to fuzzy systems. Additionally, K-nearest neighbor networks are presented as an alternative to neural networks.
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Linguistic Editors: |
Violitzi, Georgia |
Type: |
Chapter |
Creation Date: | 2015 |
Item Details: | |
License: |
http://creativecommons.org/licenses/by-nc-nd/3.0/gr |
Handle | http://hdl.handle.net/11419/3447 |
Bibliographic Reference: | Kampourlazos, V., & Papakostas, G. (2015). Classic Computational Intelligence Extensions [Chapter]. In Kampourlazos, V., & Papakostas, G. 2015. Introduction to Computational Intelligence [Undergraduate textbook]. Kallipos, Open Academic Editions. https://hdl.handle.net/11419/3447 |
Language: |
Greek |
Is Part of: |
Introduction to Computational Intelligence |
Publication Origin: |
Kallipos, Open Academic Editions |