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Committee Date Time Place Paper Title / Authors Abstract Paper #
NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] 2016-07-06
10:00		 Okinawa Okinawa Institute of Science and Technology A Supervised Learning Approach to Causal Inference for Bivariate Time Series
Yoichi Chikahara, Akinori Fujino (NTT) IBISML2016-2		 Causal inference in time series is a problem to estimate the underlying causal relationship between time-dependent varia... [more] IBISML2016-2
pp.189-194
NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] 2016-07-06
10:25		 Okinawa Okinawa Institute of Science and Technology A Semi-supervised Learning Method for Imbalanced Binary Classification
Akinori Fujino, Naonori Ueda (NTT) IBISML2016-3		 This paper presents a semi-supervised learning method for imbalanced binary classification where the number of positive ... [more] IBISML2016-3
pp.195-200
MICT, ASN, MoNA
(Joint)		 2015-01-26
10:15		 Wakayama Nanki Shirahama Estimating Effective Time of Event for Keeping Regular Customers
Hisashi Kurasawa, Takahiro Hata, Motonori Nakamura, Hiroshi Sato, Akihiro Tsutsui, Akinori Fujino, Katsuyoshi Hayashi, Koichi Takasugi (NTT) MoNA2014-61		 Various events are held in order to keep regular customers. For planning an effective event, conventional methods quanti... [more] MoNA2014-61
pp.19-24
IBISML 2011-11-10
15:45		 Nara Nara Womens Univ. Detection of miscategorized samples based on predictive likelihood maximization
Akinori Fujino, Tomoharu Iwata, Masaaki Nagata (NTT) IBISML2011-83		  [more] IBISML2011-83
pp.277-283
PRMU 2006-12-15
10:30		 Fukui   Semi-supervised classification of heterogeneous data
Akinori Fujino, Naonori Ueda, Kazumi Saito (NTT) 		 [more] PRMU2006-173
pp.13-18
PRMU, NLC 2005-02-24
11:00		 Tokyo   Optimal combination of labeled and unlabeled data for semi-supervised classification
Akinori Fujino, Naonori Ueda, Kazumi Saito (NTT) 		Unlabeled data are used to improve the accuracy of classifiers when the number of labeled data is not enough. In probabi... [more] NLC2004-100 PRMU2004-182
pp.19-24
 Results 1 - 6 of 6  /   
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