Multiscale Bagging and Its Applications
スポンサーリンク
概要
- 論文の詳細を見る
We propose multiscale bagging as a modification of the bagging procedure. In ordinary bagging, the bootstrap resampling is used for generating bootstrap samples. We replace it with the multiscale bootstrap algorithm. In multiscale bagging, the sample size m of bootstrap samples may be altered from the sample size n of learning dataset. For assessing the output of a classifier, we compute bootstrap probability of class label; the frequency of observing a specified class label in the outputs of classifiers learned from bootstrap samples. A scaling-law of bootstrap probability with respect to σ2=n/m has been developed in connection with the geometrical theory. We consider two different ways for using multiscale bagging of classifiers. The first usage is to construct a confidence set of class labels, instead of a single label. The second usage is to find inputs close to decision boundaries in the context of query by bagging for active learning. It turned out, interestingly, that an appropriate choice of m is m=-n, i.e., σ2=-1, for the first usage, and m=∞ , i.e., σ2=0, for the second usage.
論文 | ランダム
- 9)高齢者におけるTrans Radial Intervention(TRI)の検討
- 左単副腎に褐色細胞腫の発生をみた慢性血液透析患者の1例-副腎皮質温存術と慢性血液透析患者における血漿バニルマンデル酸値の検討-
- 慢性血液透析患者における褐色細胞腫の2例
- スペクトル解析を用いた透析患者の自律神経評価
- 尿管皮膚〓術の臨床的検討