Research on Applied Technology with Online Boosting Algorithms
Available Online August 2015.
- https://doi.org/10.2991/ic3me-15.2015.153How to use a DOI?
- boosting, ensemble learning,online learning, accuracy
- 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.
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 -