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Volumn 319, Issue 5866, 2008, Pages 1111-1113

Predicting human interactive learning by regret-driven neural networks

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL NEURAL NETWORK; DECISION MAKING; LEARNING; MODELING; PREDICTION;

EID: 39749189365     PISSN: 00368075     EISSN: 10959203     Source Type: Journal    
DOI: 10.1126/science.1151185     Document Type: Article
Times cited : (79)

References (29)
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    • N. Camille et al., Science 304, 1167 (2004).
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    • Camille, N.1
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    • D. Lee, Nature 441, 822 (2006).
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    • Lee, D.1
  • 28
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    • In a recent comparison of different learning models (7, NFP was tested on 10 of the games included in our data set, estimating its parameters on a different set of data. In this case, NFP did not perform better than the other models, including REL and reinforcement learning, confirming that (with two free parameters) the choice of the data set on which parameters are estimated may be a source of instability of the predictive performance of the model
    • In a recent comparison of different learning models (7), NFP was tested on 10 of the games included in our data set, estimating its parameters on a different set of data. In this case, NFP did not perform better than the other models, including REL and reinforcement learning, confirming that (with two free parameters) the choice of the data set on which parameters are estimated may be a source of instability of the predictive performance of the model.
  • 29
    • 39749121094 scopus 로고    scopus 로고
    • I. Erev, J. Ochs, and B. O'Neill kindly provided to us their original data sets. Discussions with R. Selten were a major source of inspiration for the project. P. Pellizzari, C. Gilbert, the anonymous reviewers and the editor helped us to substantially improve the paper. Ministero dell'Università e della Ricerca projects Fondo per gli Investimenti della Ricerca di Base RBNE03A9A7 and Progetti di Ricerca di Interesse Nazionale 2005139342 provided financial support for the research.
    • I. Erev, J. Ochs, and B. O'Neill kindly provided to us their original data sets. Discussions with R. Selten were a major source of inspiration for the project. P. Pellizzari, C. Gilbert, the anonymous reviewers and the editor helped us to substantially improve the paper. Ministero dell'Università e della Ricerca projects Fondo per gli Investimenti della Ricerca di Base RBNE03A9A7 and Progetti di Ricerca di Interesse Nazionale 2005139342 provided financial support for the research.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.