introduction the increasing use of satellite remote sensing data for civilian use has proved to be the most cost-effective means of mapping and monitoring for environmental changes. satellite remote sensing has played a pivotal role in finding forest cover, vegetation type and land use changes in urban areas. one of the most complete of these methods is classification. the conventional per-pixe...

貯留層流体特性の推定において，機械学習方式の性能は相関関係に基づく従来方式に匹敵する。機械学習方式のなかで，人工ニューラルネットワークモデルが最も一般的に使用されているモデルの一つであるが，ブラックボックス問題なる課題を有することが知られている。そこで本研究では，この課題を解決すべく新たなモデル化方式を検討する。正確な推定を実現すると同時に，推定結果に対する入力パラメーターとその重要度に関する考察を可能とするため，提案方式ではブースト決定木回帰（BDTR）を用いて，石油とガスの比重，石油と溶解ガスの比，貯留層温度の関数として，沸点圧力（Pb）ならびに沸点圧力における石油容積係数（Bob）を推定する。構築されたBDTRモデルは，既存の機械学習モデルや最も一般的に使用されている相関関係に基づく従来手法と比べて，PbおよびBobをより高精度に推定可能である。特にPbの推定においては，欠損値...

Scaling regression to large datasets is a common problem in many application areas. We propose a two step approach to scaling regression to large datasets. Using a regression tree (CART) to segment the large dataset constitutes the first step of this approach. The second step of this approach is to develop a suitable regression model for each segment. Since segment sizes are not very large, we ...

Boosting is a means of constructing a strong classi er by aggregating a sequence of weaker classi ers. The sequence evolves by focusing the attention of a base learning algorithm on remaining examples that are hardest to classify. Boosting algorithms can thus be used to enhance the performance of a simple classi er (typically a regression tree or tree stump) in an automatic way. This makes boos...

Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1). Consequently, beta reg...