On the Bias of Predictive Distribution in Pattern Recognition
| 言語 | 英語 |
|---|---|
| 種類 | その他 |
| 著者 | Masakazu Iwamura，Shinichiro Omachi，Hirotomo Aso |
| 論文名 | On the Bias of Predictive Distribution in Pattern Recognition |
| 論文誌名 | Systems and Computers in Japan |
| Vol. | 36 |
| No. | 5 |
| ページ | pp.45-54 |
| 出版社 | John Wiley & Sons, Inc. |
| 査読の有無 | 無 |
| 年月 | 2005年5月 |
| 分野 | パターン認識 |
| キー | Iwamura2005 (id=329) |
要約
To estimate distribution from training samples, the maximum likelihood estimation treats the unknown parameter of the distribution as a constant. On the other hand, Bayesian estimation treats the parameter as a random variable. In pattern recognition, Bayesian estimation has been known to improve recognition accuracy. However, it was pointed out that the Bayesian estimation is not effective due to the bias of the likelihood when sample sizes of classes are not the same. In this paper, we show recognition accuracy is improved by modifying the bias of the likelihood when sample sizes are not the same. This indicates that the cause of the ineffectiveness is the bias. We derive the formula of the bias of Geisser's predictive distribution without any approximation, and show a way of modification of the bias of the likelihood. We confirm effectiveness of the proposed method in experiments. In addition, the derived formula gives the theoretical background of a known empirical knowledge.
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