抜粋
In this paper, we propose a Laplacian minimax probability machine, which is a semi-supervised version of minimax probability machine based on the manifold regularization framework. We also show that the proposed method can be kernelized on the basis of a theorem similar to the representer theorem for non-linear cases. Experiments confirm that the proposed methods achieve competitive results, as compared to existing graph-based learning methods such as the Laplacian support vector machine and the Laplacian regularized least square, for publicly available datasets from the UCI machine learning repository.
| 元の言語 | English |
|---|---|
| ページ（範囲） | 192-200 |
| ページ数 | 9 |
| ジャーナル | Pattern Recognition Letters |
| 巻 | 37 |
| 発行部数 | 1 |
| DOI | |
| 出版物ステータス | Published - 2014 2 1 |
ASJC Scopus subject areas
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence
フィンガープリント Laplacian minimax probability machine' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。
これを引用
Yoshiyama, K., & Sakurai, A. (2014). Laplacian minimax probability machine. Pattern Recognition Letters, 37(1), 192-200. https://doi.org/10.1016/j.patrec.2013.01.004