Proceedings of the 3rd International Conference on Material, Mechanical and Manufacturing Engineering

Research on Applied Technology with Online Boosting Algorithms

Authors
Xiaowei Sun
Corresponding Author
Xiaowei Sun
Available Online August 2015.
DOI
https://doi.org/10.2991/ic3me-15.2015.153How to use a DOI?
Keywords
boosting, ensemble learning,online learning, accuracy
Abstract
Boosting is an effective classifier combination method, which can improve classification performance of an unstable learning algorithm due to its theoretical performance guarantees and strong experimental results. However, the algorithm has been used mainly in batch mode, i.e., it requires the entire training set to be available at once and, in some cases, require random access to the data. Recently, Nikunj C.oza(2001) proved that some preliminary theoretical results and some empirical comparisons of the classification accuracies of online algorithms with their corresponding batch algorithms on many datasets. In this paper, we present online versions of some boosting methods that require only one pass through the training data. Specifically, we discuss how our online algorithms mirror the techniques that boosting use to generate multiple distinct base models. Our online algorithms are demonstrated to be more practical with larger datasets.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
3rd International Conference on Material, Mechanical and Manufacturing Engineering (IC3ME 2015)
Part of series
Advances in Engineering Research
Publication Date
August 2015
ISBN
978-94-6252-100-1
ISSN
2352-5401
DOI
https://doi.org/10.2991/ic3me-15.2015.153How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Xiaowei Sun
PY  - 2015/08
DA  - 2015/08
TI  - Research on Applied Technology with Online Boosting Algorithms
BT  - 3rd International Conference on Material, Mechanical and Manufacturing Engineering (IC3ME 2015)
PB  - Atlantis Press
SP  - 798
EP  - 803
SN  - 2352-5401
UR  - https://doi.org/10.2991/ic3me-15.2015.153
DO  - https://doi.org/10.2991/ic3me-15.2015.153
ID  - Sun2015/08
ER  -