Optimal Boundary SVM Incremental Learning Algorithm
Year: 2013
Type: article
Abstract: Support vectors (SVs) cant be selected completely in support vector machine (SVM) incremental, resulting incremental learning process cant be sustained. In order to solve this problem, the article proposes optimal boundary SVM incremental learning algorithm. Based on in-depth analysis of the trend of the classification surface and make use of the KKT conditions, selecting the border of the vectors include the support vectors to participate SVM incremental learning. The experiment shows that the algorithm can be completely covered the support vectors and have the identical result with the classic support vector machine, it also saves lots of time. Therefore it can provide the conditions for future large sample classification and incremental learning sustainability.
Source: Applied Mechanics and Materials
Authors: Jian Cao, Shi Yu Sun, Xiu Sheng Duan
Language: English
Topic: Machine Learning and ELM
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W1999956603