| Budget Amount *help |
¥5,070,000 (Direct Cost: ¥3,900,000、Indirect Cost: ¥1,170,000)
Fiscal Year 2015: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2014: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2013: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2012: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Outline of Final Research Achievements |
We had proposed the multiobjective multiclass support vector machine (MMSVM), which can maximize the geometric margins for multiclass classification problem. In this study, we developed reduction methods of time and space computational complexity of the MMSVM, and extended the MMSVM into soft-margin models which can learn training data including outliers, where we compare the performances of various combinations from some restriction methods of the constraints of the MMSVM, several solving method of the multiobjective optimization, and two kinds of penalty functions. Among them, we verified that some propose methods, a data reduction method based on support vectors for the soft-margin MMSVM, an improved MMSVM based on the one-against-all method, an MMSVM based on the k-means method are more effective than existing methods.
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