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Evolutionary Intelligence

, Volume 12, Issue 2, pp 97–112 | Cite as

Incremental supervised learning: algorithms and applications in pattern recognition

  • Aida ChefrourEmail author
Review Article
  • 133 Downloads

Abstract

The most effective well-known methods in the context of static machine learning offer no alternative to evolution and dynamic adaptation to integrate new data or to restructure problems already partially learned. In this area, the incremental learning represents an interesting alternative and constitutes an open research field, becoming one of the major concerns of the machine learning and classification community. In this paper, we study incremental supervised learning techniques and their applications, especially in the field of pattern recognition. This article presents an overview of the main concepts and supervised algorithms of incremental learning, including a synthesis of research studies done in this field and focusing on neural networks, decision trees and support vector machines.

Keywords

Machine learning Incremental clustering Pattern recognition Neural network Decision tree SVM 

Notes

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.LISCO Laboratory, Computer Science DepartmentBadji Mokhtar-Annaba UniversityAnnabaAlgeria
  2. 2.Computer Science DepartmentMohamed Cherif Messaadia UniversitySouk AhrasAlgeria

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