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Research Publication and Presentation |
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Typhoon Analysis and Data Mining with Kernel Methods
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.
Citation
Asanobu KITAMOTO,
"Typhoon Analysis and Data Mining with Kernel Methods",
Pattern Recognition with Support Vector Machines (SVM2002), Lecture Notes in Computer Science (LNCS) 2388,
Lee, S.W., and Verri, A. (Eds.),
pp. 237-248,
Springer,
doi:10.1007/3-540-45665-1_18,
2002-8
BibTeX Format
@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 = {},
}
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