Improvement of Efficiency for Interactive Document Retrieval using Transductive Inference
| Project/Area Number |
16500094
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Central Research Institute of Electric Power Industry |
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Principal Investigator |
ONODA Takashi Central Research Institute of Electric Power Industry, System Engineering Laboratory, Senior Research Scientist, システム技術研究所, 上席研究員 (40371661)
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| Co-Investigator(Kenkyū-buntansha) |
YAMADA Seiji National Institute of Informatics, Intelligent Systems Research Division, Professor, 知能システム研究系, 教授 (50220380)
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| Project Period (FY) |
2004 – 2005
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| Project Status |
Completed (Fiscal Year 2005)
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| Budget Amount *help |
¥3,700,000 (Direct Cost: ¥3,700,000)
Fiscal Year 2005: ¥1,800,000 (Direct Cost: ¥1,800,000)
Fiscal Year 2004: ¥1,900,000 (Direct Cost: ¥1,900,000)
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| Keywords | document retrieval / support vector machine / interaction / transductive inference / active learning / 適合性フィードバック / One-Class classification |
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| Research Abstract |
In the past, traditional interactive document retrieval system retrieved the remained documents by using only the documents that the user evaluated, and had not used information in the remained documents positively. However, the methods using these documents, which are not evaluated by a user, in recent years is proposed in the machine learning research field. The retrieval efficiency can be expected to be improved rapidly by using these methods based on the not evaluated documents. In addition, the interactive document retrieval system should be designed with considering the recognition load of human being In this research, the research purpose is to research and develop the interactive document retrieval method based on the transductive inference using the artificial intelligence methods, especially the machine learning methods.
In such a purpose, the concept of the transductive inference was introduced into the frame of active learning with the support vector machine in this research. We proposed a novel document selection method which can display the documents, which are near the user's desire and the system's desire. And we developed this method on a computer and evaluated this method using large bench mark datasets.
By the achievement of this research, the user input the keywords to retrieve documents onetime. After this input, the user evaluates the displayed documents and the user can see the relevant documents. Therefore, the user escapes from making keywords to retrieve the documents.
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Report
(3 results)
Research Products
(25 results)
