An incremental learning algorithm based on Support Vector Machine for pattern recognition

Lamei Zou; Tianxu Zhang; Zhiguo Cao

doi:10.1117/12.832348

30 October 2009 An incremental learning algorithm based on Support Vector Machine for pattern recognition

Lamei Zou, Tianxu Zhang, Zhiguo Cao

Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 74961H (2009) https://doi.org/10.1117/12.832348
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

With the advent of information age, especially with the rapid development of network, "information explosion" problem has emerged. How to improve the classifier's training precision steadily with accumulation of the samples is the original idea of the incremental learning. Support Vector Machine (SVM) has been successfully applied in many pattern recognition fields. While its complex computation is the bottle-neck to deal with large-scale data. It's important to do researches on the SVM's incremental learning. This article proposes a SVM's incremental learning algorithm based on the filtering fixed partition of the data set. This article firstly presents "Two-class problem"s algorithm and then generalizes it to the "Multiclass problem" algorithm by the One-vs-One method. The experimental results on three types of data sets' classification show that the proposed incremental learning technique can greatly improve the efficiency of SVM learning. SVM Incremental learning can not only ensure the correct identification rate but also speedup the training process.

Citation Download Citation

Lamei Zou, Tianxu Zhang, and Zhiguo Cao "An incremental learning algorithm based on Support Vector Machine for pattern recognition", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74961H (30 October 2009); https://doi.org/10.1117/12.832348

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available