Open AccessApplication of improved symmetric incremental learning algorithm in glass defect classification
Author(s) -
Xun Lian
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/330/5/052043
Subject(s) - correctness , algorithm , computer science , support vector machine , machine learning , field (mathematics) , incremental learning , artificial intelligence , pattern recognition (psychology) , data mining , mathematics , pure mathematics
This paper focuses on the application and development of image recognition technology in glass defect detection industry, and studies the basic theory and implementation method of support vector machine. Aiming at the main shortcomings of incremental learning algorithm in current support vector machines, an improved symmetric incremental learning algorithm (S-ISVM) is designed. Support vector machines are selected as training tools and classification tools, and the training samples and test samples are used as prediction samples respectively. The feasibility of the algorithm and the correctness and validity of data processing methods are verified. The research results have broad prospects for development and practical value in the field of glass industry production.