Applying a Machine Learning Technique to Classification of Japanese Pressure Patterns

In climate research, pressure patterns are often very important. When a climatologists need to know the days of a specific pressure pattern, for example "low pressure in Western areas of Japan and high pressure in Eastern areas of Japan (Japanese winter-type weather)," they have to visually check a huge number of surface weather charts. To overcome this problem, we propose an automatic classification system using a support vector machine (SVM), which is a machine-learning method. We attempted to classify pressure patterns into two classes: "winter type" and "non-winter type". For both training datasets and test datasets, we used the JRA-25 dataset from 1981 to 2000. An experimental evaluation showed that our method obtained a greater than 0.8 F-measure. We noted that variations in results were based on differences in training datasets

平成20年度 計算科学研究センター研究報告

1-1 素粒子分野1-2 宇宙分野2-1 計算物性分野2-2 原子核理論分野2-3 計算生命分野3-1 地球環境分野3-2 生物分野4-1 計算機アーキテクチャ研究分野5-1 計算知能分野5-2 計算メディア分

筑波大学計算科学研究センター 平成21年度 年次報告書

1.平成21年度 基本方針、重点施策・改善目標等　……　42.平成21年度 実績報告　……　71.素粒子宇宙研究部門　……　131.1.素粒子分野　……　131.2.宇宙分野　……　262.物質生命研究部門　……　482.1.物質工学理論グループ　……　482.2.生命物理グループ　……　552.3.計算物性グループ　……　662.4.原子核理論グループ　……　803.地球生物環境研究部門　……　883.1.地球環境学分野　……　883.2.生物分野　……　964.超高速計算システム研究部門　……　1025.計算情報学研究部門　……　1105.1.計算知能分野　……　1105.2.計算メディア分野　……　12