Least squares twin multi-class classification support vector machine

Abstract. In view of the batch implementations of standard support vector machine must be retrained from scratch every time when the training set is incremental modified, an incremental learning algorithm based on least squares twin multi-class classification support vector machine (ILST-KSVC) is proposed by solving two inverse matrix. The method will be applied on online environment to update initial data, which avoided cumbersome double counting. ILST-KSVC inherited the advantages of the basic algorithm and has some merits of Least square twin support vector machine for excellent performance on training speed and support vector classification regression for K-class's well classification accuracy. The result will be confirmed no matter in low dimension or in high dimension in UCI datasets.

show abstract

“…According to formula (11) and formula (12), in nonlinear LST-KSVC, we can easily get the new weight and bias after updating, like formula (24) …”

Section: Non-linear Ilst-ksvcmentioning

confidence: 99%

“…In order to reduce the computational complexity and simplified the structure of classifier further, Jalal a. etc. combined with the LS-TSVM and K-SVCR put forward Least squares twin multi-class classification support vector machine (LST-KSVC) [12].LST-KSVCR inherited the faster training speed of LS-TSVM and high accuracy of KSVC.…”

Section: Introductionmentioning

confidence: 99%

Incremental Learning Algorithm of Least Square Twin KSVC

Wang

Yang

Chen

et al. 2016

MATEC Web of Conferences

View full text Add to dashboard Cite

show abstract

“…Applications of TWSVM are used in various field and it is a very useful approach for the pattern classification [8][9][10]. The goal of TWSVM is to construct a model to predict the class label of given data samples.…”

Section: In 1990smentioning

confidence: 99%

Intrusion Detection Based on Improved Least Squares Multi-Classification Twin Support Vector Machine

2016

Jour. Math. Sci. Adv. Appl.

View full text Add to dashboard Cite

show abstract

“…Originally, the SVM is designed to solve binary classification problems, but multi-class extensions are also available [63]. Hereafter, the most related studies in SVM techniques and uncertainty of input data are reviewed.…”

Section: Svm In Qa and Data Classificationmentioning

confidence: 99%

Review of data mining applications for quality assessment in manufacturing industry: support vector machines

Rostami

Dantan

Homri

2015

Int. J. Metrol. Qual. Eng.

View full text Add to dashboard Cite

is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. AbstractIn many modern manufacturing industries, data that characterize the manufacturing process are electronically collected and stored in the databases.Due to advances in data collection systems and analysis tools, data mining (DM)has widely been applied for quality assessment (QA) in manufacturing industries.In DM, the choice of technique to use in analyzing a dataset and assessing the quality depend on the understanding of the analyst. On the other hand, with the advent of improved and efficient prediction techniques, there is a need for an analyst to know which tool performs best for a particular type of data set.Although a few review papers have recently been published to discuss DM applications in manufacturing for QA, this paper provides an extensive review to investigate the application of a special DM technique, namely support vector machine (SVM) to solve QA problems. The review provides a comprehensive analysis of the literature from various points of view as DM preliminaries, data preprocessing, DM applications for each quality task, SVM preliminaries, and application results. Summary tables and figures are also provided besides to the analyses. Finally, conclusions and future research directions are provided.

show abstract

Least squares twin multi-class classification support vector machine

Cited by 76 publications

References 24 publications

Incremental Learning Algorithm of Least Square Twin KSVC

Incremental Learning Algorithm of Least Square Twin KSVC

Intrusion Detection Based on Improved Least Squares Multi-Classification Twin Support Vector Machine

Review of data mining applications for quality assessment in manufacturing industry: support vector machines

Contact Info

Product

Resources

About