| Project/Area Number |
18500109
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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 | The University of Electro-Communications |
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Principal Investigator |
NISHINO Tetsuro The University of Electro-Communications, Faculty of Electro-Communications, Professor (10198484)
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| Co-Investigator(Kenkyū-buntansha) |
SASAHARA Kazutoshi RIKEN, Brain Science Institute, 脳科学総合研究センター, Researcher (60415172)
TAKAHASHI Miki RIKEN, Brain Science Institute, 脳科学総合研究センター, Researcher (90415216)
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| Project Period (FY) |
2006 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥4,010,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥510,000)
Fiscal Year 2007: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
Fiscal Year 2006: ¥1,800,000 (Direct Cost: ¥1,800,000)
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| Keywords | Bird Song Syntax / Phonetics / Language Acquisition / Computational Learning Theory / Phonetic Extraction / Noise Reduction / 計算論敵学習理論 |
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| Research Abstract |
We propose an efficient automata-based approach to extract behavioral units and rules from continuous sequential data of animal behavior. Introducing original extensions, we integrate two elemental methods-the Ngram model and the Angluin's machine learning algorithm into an ethological data mining framework. It allows us to obtain the simplest finite automaton representation of behavioral rule that accepts (or generates) the smallest set of possible behavioral patterns from sequential data of animal behavior. With this method, we demonstrate how the ethological data mining works using real birdsong data and performs experimental evaluations of this method using artificial birdsong data generated by a computer program. These results suggest that our ethological data mining effectively works even for noisy ethological data by appropriately setting the parameters. In addition, we demonstrate a case study using the Bengalese finch song, showing that our method successfully grasps the core structure of the singing behavior such as loops and branchings.
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