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All Technical Committee Conferences (Searched in: All Years)
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Search Results: Conference Papers |
Conference Papers (Available on Advance Programs) (Sort by: Date Descending) |
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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 |
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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 |
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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 |
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