| Project/Area Number |
13640117
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
General mathematics (including Probability theory/Statistical mathematics)
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| Research Institution | HIROSHIMA UNIVERSITY |
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Principal Investigator |
NISHII Ryuei Hiroshima University, Faculty of Integrated Arts and Sciences, Professor, 総合科学部, 教授 (40127684)
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| Co-Investigator(Kenkyū-buntansha) |
ASANO Akira Hiroshima University, Faculty of Integrated Arts and Sciences, Associate Professor, 総合科学部, 助教授 (60243987)
KUWADA Masahide Hiroshima University, Faculty of Integrated Arts and Sciences, Professor, 総合科学部, 教授 (10144891)
TANAKA Shojiro Shimane University, Interdisciplinary Faculty of Science, 総合理工学部, 教授 (00197427)
SHIMA Tadashi Hiroshima University, Faculty of Integrated Arts and Sciences, Associate Professor, 総合科学部, 助教授 (30226196)
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| Project Period (FY) |
2001 – 2002
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| Project Status |
Completed (Fiscal Year 2002)
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| Budget Amount *help |
¥3,200,000 (Direct Cost: ¥3,200,000)
Fiscal Year 2002: ¥1,500,000 (Direct Cost: ¥1,500,000)
Fiscal Year 2001: ¥1,700,000 (Direct Cost: ¥1,700,000)
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| Keywords | AdaBoost / Image segmentation / MAP estimate / Markov random filed / Spatial dependency / サポートベクターマシン / AdaBoost / image segmentation / MAP estimate / Markov random field / Spatial dependency / Support vector machine / Data fusion / ICM / MAP estmate |
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| Research Abstract |
The main aim of this study was to derive an efficient classification procedure for land-cover categories based on geospatial date. Assuming normal distributions for feature vectors and a structural Markov random field (MRF) for category distribution, we obtained a classification method, which shows an excellent performance for real date. Then, we proceeded to examine general structural MRF and an estimation method of unknown parameters. These results were presented as invited talks at two international conferences (submitted for publication).
It was shown that MRF models the spatial dependency of the categories well. Next, we take machine-learning approach for feature space. Probabilistic support vector machine (SVM ) is treated and the posterior probability is defined by the loss function. This approach gives a similar efficiency due to the statistical approach. These is, however, a room for improvement of the posterior. This point is now under investigation.
Another attention was paid for microarray data on human gene. The aim is to classify cell types by gene expression patterns through machine learning. First, dozens of genes are selected among several thousands genes by AdaBoost. Then, SVM chooses the best combination of the selected genes. Our approach is highly efficient than the ordinary (in preparation).
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