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幼児の学習バイアスを利用したエージェントによる語意学習の効率化
https://nitech.repo.nii.ac.jp/records/5359
https://nitech.repo.nii.ac.jp/records/535914cceb5c-7ae0-4cd9-af02-ca111f4143d7
名前 / ファイル | ライセンス | アクション |
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本文_fulltext (899.7 kB)
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Copyright c 社団法人人工知能学会
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2012-11-06 | |||||
タイトル | ||||||
タイトル | 幼児の学習バイアスを利用したエージェントによる語意学習の効率化 | |||||
言語 | ||||||
言語 | jpn | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | journal article | |||||
その他(別言語等)のタイトル | ||||||
その他のタイトル | ヨウジ ノ ガクシュウ バイアス オ リヨウシタ エージェント ニヨル ゴイ ガクシュウ ノ コウリツカ | |||||
その他(別言語等)のタイトル | ||||||
その他のタイトル | Efficient Learning of Word Meanings by Agents Using Biases Observed in Language Development of Children | |||||
著者 |
田口, 亮
× 田口, 亮× 木村, 優志× 小玉, 智志× 篠原, 修二× 入部, 百合絵× 桂田, 浩一× 新田, 恒雄 |
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著者別名 | ||||||
姓名 | Taguchi, Ryo | |||||
書誌情報 |
人工知能学会論文誌 / 人工知能学会 巻 22, 号 4, p. 444-453, 発行日 2007-11-01 |
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出版者 | ||||||
出版者 | 人工知能学会 | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 13460714 | |||||
書誌レコードID(NCID) | ||||||
収録物識別子タイプ | NCID | |||||
収録物識別子 | AA11579226 | |||||
著者版フラグ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
DOI | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | DOI | |||||
関連識別子 | http://dx.doi.org/10.1527/tjsai.22.444 | |||||
関連名称 | 10.1527/tjsai.22.444 | |||||
内容記述 | ||||||
内容記述タイプ | Other | |||||
内容記述 | Recently, studies on learning of word meanings by agents have begun. In these studies, a human shows objects to an agent and utters words such as red or box. The agent finds out object's feature represented by each spoken word. In our method, firstly, the agent learns probability distribution p(x) and conditional probability distribution p(x|w), where x is an object feature and w is a word. If a word w does not represent a feature x, p(x) and p(x|w) will be almost same distribution because x is independent of w. This fact enables the agent to use distance between p(x) and p(x|w) when inferring which feature the word represents. Previous works also employ similar stochastic approaches to detect the feature. However, such approaches need a lot of examples to learn correct distributions. | |||||
フォーマット | ||||||
内容記述タイプ | Other | |||||
内容記述 | application/pdf |