Distributed Incremental wLPSVM Learning

Loading...
Thumbnail Image
Other Title
Authors
Zhu, Lei
Ban, Tao
Ikeda, K.
Pang, P.
Sarrafzadeh, Hossein
Author ORCID Profiles (clickable)
Degree
Grantor
Date
2016-12
Supervisors
Type
Conference Contribution - Paper in Published Proceedings
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
wLPSVM
weighted linear proximal support vector machines
class imbalance learning
distributed incremental learning
ANZSRC Field of Research Code (2020)
Citation
Zhu, L., Ban, T., Ikeda, K., Pang, P., & Sarrafzadeh, A. (2016, December). Distributed Incremental wLPSVM Learning. - (Ed.), IEEE Symposium Series on Computational Intelligence (SSCI) (paper 45)
Abstract
Weighted linear proximal support vector machine (wLPSVM) is known as an efficient binary classification algorithm with good accuracy and class-imbalance robustness. In this work, original batch wLPSVM is facilitated with distributed incremental learning capability, which allows simultaneously learning from multiple streaming data sources that are geographically distributed. In our approach, incremental and distributed learning are solved as a merging problem at the same time. A new wLPSVM expression is derived. In the new expression, knowledge from samples are presented as a set of class-wised core matrices, and merging knowledge from two subsets of data can be simply accomplished by matrix addition. With the new expression, we are able to conduct incremental and distributed learning at the same time via merging knowledge from multiple incremental stages and multiple data sources.
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link to ePress publication
DOI
Copyright holder
Authors
Copyright notice
All rights reserved
Copyright license
Available online at
This item appears in: