| タイトル | A Learning with Membership Queries to Minimize Prediction Error |
|---|---|
| 著者 | 浮田善文 、松嶋敏泰 、平澤茂一 |
| 年度 | 1997 |
| 形式 | 国際学会 |
| 分野 | 知識情報処理 |
| 掲載雑誌名 | Proceedings of IEEE International Conference on Systems, Man and Cybernetics |
| 掲載号・ページ | vol.5, pp.4412-4417, Florida, USA |
| 掲載年 | 1997 |
| 掲載月 | 10 |
| アブスト （日本語） |
学会名：1997 IEEE International Conference on Systems, Man and Cybernetics 日程：1997年10月12日～1997年10月15日 場所：Florida, USA |
| アブスト （英語） |
In this paper, we consider the problem to predict the class of an unknown sample after learning from queries. We propose to evaluate a learning algorithm by a loss function for the prediction under a constraint. In this paper, the error probability for the prediction and the number of queries is defined as the loss function and the constraint, respectively. Then our objective is to minimize the error probability, the error probability is determined by what presentation order for instances to query and how to predict. Since the optimal prediction has been shown in previous researches, we only have to select the optimal presentation order for instances to query. We propose a lower bound used in the branch-and-bound algorithm to select the optimal presentation order for instances. Lastly, we show the efficiency of the algorithm using the derived lower bound by numerical computation. |
| 備考 （日本語） |
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| 備考 （英語） |
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