| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
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: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Outline of Final Research Achievements |
We proposed a database-Hadoop hybrid approach to scalable machine learning where batch-learning is performed on the Hadoop platform, while incremental-learning is performed on PostgreSQL.
We conducted a series of exterimental evaluation using a commercial advertisement dataset provided in the KDD Cup 2012, Track 2. The experimental results show that our scheme has a superior training speed compared with state-of-the-art scalable machine learning frameworks, 5 and 7.65 times faster than Vowpal Wabbit and Bismarck, respectively, for a regression task.
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