| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2012: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2011: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2010: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Research Abstract |
In this project, we have developed a principle approach for learning from non-IID data of large-scale information sources. The algorithms developed from this principle were applied to the concrete subjects of physical behaviors of people and autonomous agents. The details of the principles and the algorithms have been published in two international conferences proceedings and with an international journal paper (1). The developed algorithms and benchmark datasets are made public on our website. The algorithms can address the problems of detecting anomalous behaviors and detecting context-specific distributions of behavior patterns, respectively. Furthermore, we developed a general representation model for conducting non-IID data learning presented at oral presentation (2), and a classification model for addressing time-sensitive classification problems presented at oral presentation (1).
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