Incremental and Distributed Learning with Support Vector Machines
Abstract
Due to the increase in the amount of data gathered every \nday in the real world problems (e.g., bioinformatics), there is a need for inductive learning algorithms that can incrementally process large amounts of data that is being accumulated over time in physically distributed, autonomous data repositories. In the incremental setting, the learner gradually refines a hypothesis (or a set of hypotheses) as new data become available. Because of the large volume of data involved, it may not be practical to store and access the entire dataset during learning. Thus, the learner does not have access to data that has been encountered at a previous time.
Cite
Text
Caragea et al. "Incremental and Distributed Learning with Support Vector Machines." AAAI Conference on Artificial Intelligence, 2000.Markdown
[Caragea et al. "Incremental and Distributed Learning with Support Vector Machines." AAAI Conference on Artificial Intelligence, 2000.](https://mlanthology.org/aaai/2000/caragea2000aaai-incremental/)BibTeX
@inproceedings{caragea2000aaai-incremental,
title = {{Incremental and Distributed Learning with Support Vector Machines}},
author = {Caragea, Doina and Silvescu, Adrian and Honavar, Vasant G.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2000},
pages = {1067},
url = {https://mlanthology.org/aaai/2000/caragea2000aaai-incremental/}
}