Machine Learning for Small Data
Author:
Affiliation:
1. Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST)
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Subject
General Earth and Planetary Sciences,General Environmental Science
Link
https://www.jstage.jst.go.jp/article/essfr/16/4/16_247/_pdf
Reference44 articles.
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2. 2) C.C. Aggarwal, A. Hinneburg, and D.A. Keim, “On the surprising behavior of distance metrics in high dimensional space,” International Conference on Database Theory, 2001.
3. 3) 小西貞則，情報量規準（予測と発見の科学），朝倉書店，2004.
4. 4) C.M. Hurvich, R. Shumway, and C.-L. Tsai, “Improved estimators of Kullback-Leibler information for autoregressive model selection in small samples,” Biometrika, vol.77, no.4, pp.709-719, 1990.
5. 5) S. Watanabe, “A widely applicable Bayesian information criterion,” J. Mach. Learn. Res., vol.14, no.27, pp.867-897, 2013.
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