| Budget Amount *help |
¥12,350,000 (Direct Cost: ¥9,500,000、Indirect Cost: ¥2,850,000)
Fiscal Year 2014: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
Fiscal Year 2013: ¥5,330,000 (Direct Cost: ¥4,100,000、Indirect Cost: ¥1,230,000)
Fiscal Year 2012: ¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
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| Outline of Final Research Achievements |
In order to cope with increasing quantity and variety of data, it is important to develop information technology which enables effective reuse of learned knowledge. We have developed a transfer learning method based on the structure of feature space. In the developed method, features are learned from the given data in the source domain, and a graph is constructed based on the learned features. Then, the method tries to preserve the structure of feature space as much as possible between the source domain and the target domain in transfer learning. Under the framework of optimization learning, we have developed a transfer learning algorithm based on multiplicative update rules. The algorithm has been implemented as a prototype system, and experiments over the prototype system were conducted over several benchmark datasets. The results of the experiments indicate the effectiveness of the developed transfer learning method.
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