Catenary Support Vector Machines
Abstract
Many problems require making sequential decisions. For these problems, the benefit of acquiring further information must be weighed against the costs. In this paper, we describe the catenary support vector machine (catSVM), a margin-based method to solve sequential stopping problems. We provide theoretical guarantees for catSVM on future testing examples. We evaluated the performance of catSVM on UCI benchmark data and also applied it to the task of face detection. The experimental results show that catSVM can achieve a better cost tradeoff than single-stage SVM and chained boosting.
Cite
Text
Kan and Shelton. "Catenary Support Vector Machines." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2008. doi:10.1007/978-3-540-87479-9_57Markdown
[Kan and Shelton. "Catenary Support Vector Machines." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2008.](https://mlanthology.org/ecmlpkdd/2008/kan2008ecmlpkdd-catenary/) doi:10.1007/978-3-540-87479-9_57BibTeX
@inproceedings{kan2008ecmlpkdd-catenary,
title = {{Catenary Support Vector Machines}},
author = {Kan, Kin Fai and Shelton, Christian R.},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2008},
pages = {597-610},
doi = {10.1007/978-3-540-87479-9_57},
url = {https://mlanthology.org/ecmlpkdd/2008/kan2008ecmlpkdd-catenary/}
}