The analysis of the typhoon is based on the manual pattern recognition of cloud patterns on meteorological satellite images by human experts, but this process may be unstable and unreliable, and we think could be improved by taking advantage of both the large collection of past observations and the state-of-the-art machine learning methods, among which kernel methods, such as support vector machines (SVM) and kernel PCA, are the focus of the paper. To apply the "learning-from-data" paradigm to typhoon analysis, we built the collection of more than 34,000 "well-framed" typhoon images to be used for spatio-temporal data mining of typhoon cloud patterns with the aim of discovering hidden and unknown regularities contained in large image databases. In this paper, we deal with the problem of visualizing and classifying typhoon cloud patterns using kernel methods. We compare preliminary results with baseline algorithms, such as principal component analysis and a k-NN classifier, and discuss experimental results with the future direction of research.

      @InProceedings{ k:svm02,
      author = {Asanobu KITAMOTO},
      title = {Typhoon Analysis and Data Mining with Kernel Methods},
      booktitle = {Pattern Recognition with Support Vector Machines (SVM2002), Lecture Notes in Computer Science (LNCS) 2388},
      pages = {237-248},
      publisher = {Springer},
      editor = {Lee, S.W., and Verri, A.},
      year = 2002,
      month = 8,
      doi = {10.1007/3-540-45665-1_18},
      note = { (in English)},
      }