頭部運動機能特徴に基づく対話者の主観的印象の予測・説明モデルの構築
Author:
Affiliation:
1. Yokohama National University
2. Nippon Telegraph and Telephone Corporation
Publisher
Japanese Society for Artificial Intelligence
Subject
Artificial Intelligence,Software
Link
https://www.jstage.jst.go.jp/article/tjsai/38/3/38_38-3_H-M74/_pdf
Reference28 articles.
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2. [Barredo Arrieta 20] Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., and Herrera, F.: Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI, Inf. Fusion, Vol. 58, No. C, pp. 82-115 (2020)
3. [Breiman 01] Breiman, L.: Random Forests, Machine Learning, Vol. 45, pp. 5-32 (2001)
4. [Chen 16] Chen, T. and Guestrin, C.: XGBoost: A Scalable Tree Boosting System, in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, pp. 785-794, Association for Computing Machinery (2016)
5. [Christoph 19] Christoph, M.: Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, Lulu.com (2019)
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