| Budget Amount *help |
¥17,940,000 (Direct Cost: ¥13,800,000、Indirect Cost: ¥4,140,000)
Fiscal Year 2018: ¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2017: ¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2016: ¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2015: ¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
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| Outline of Final Research Achievements |
In this research, we aimed to achieve advanced analysis and prediction methods for real-world large-scale complex data, such as collections of text/multimodal data with structure or attributes and network data with attributed vertices or edges, by estimating probabilistic latent structure underlying the data. We especially addressed techniques and applications for social media and financial data, focusing on Bayesian models assuming prior hierarchies and deep generative models assuming latent hierarchical structure.
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