2013年
Infinitesimal annealing for training semi-supervised support vector machines
30th International Conference on Machine Learning, ICML 2013
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- 巻
- 号
- PART 3
- 開始ページ
- 1934
- 終了ページ
- 1942
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
The semi-supervised support vector machine (S3VM) is a maximum-margin classification algorithm based on both labeled and unlabeled data. Training S3VM involves either a combinatorial or non-convex optimization problem and thus finding the global optimal solution is intractable in practice. It has been demonstrated that a key to successfully find a good (local) solution of S3VM is to gradually increase the effect of unlabeled data, à la annealing. However, existing algorithms suffer from the trade-off between the resolution of annealing steps and the computation cost. In this paper, we go beyond this trade-off by proposing a novel training algorithm that efficiently performs annealing with an infinitesimal resolution. Through experiments, we demonstrate that the proposed infinitesimal annealing algorithm tends to produce better solutions with less computation time than existing approaches. Copyright 2013 by the author(s).
- リンク情報
- ID情報
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- SCOPUS ID : 84897490866