A new SVM multiclass incremental learning algorithm
type: Item
instance of: scholarly article
title: A new SVM multiclass incremental learning algorithm
zbMATH Open document ID: 1394.68308
DOI: 10.1155/2015/745815
author: Yuping Qin
author: Dan Li
author: Aihua Zhang
published in: Mathematical Problems in Engineering
publication date: 2018-08-27T00:00:00Z
review text: Summary: A new support vector machine (SVM) multiclass incremental learning algorithm is proposed. To each class training sample, the hyperellipsoidal classifier that includes as many samples as possible and pushes the outlier samples away is trained in the feature space. When the new samples are added to the classification system, the algorithm reuses the old classifiers that have nothing to do with the new sample classes. To be classified sample, the Mahalanobis distances are used to decide the class of classified sample. If the sample point is not surrounded by any hyperellipsoidal or is surrounded by more than one hyperellipsoidal, the membership is used to confirm its class. The experimental results show that the algorithm has higher performance in classification precision and classification speed.
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 90C90
zbMATH DE Number: 6927146
Wikidata QID: Q59119794
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/745815
OpenAlex ID: W1779785116
cites work: Q4935997
cites work: Q5320802
cites work: Q5485213
cites work: Data-driven adaptive observer for fault diagnosis
description: scientific article; zbMATH DE number 6927146