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.

Information

Item Type:	Journal
Divisions:	» Faculty of Science and Technology
ePrint ID:	3717
Date Deposited:	2022-12-14
Further Information:	Google Scholar
URI:	https://www.univ-soukahras.dz/en/publication/article/3717

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