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Committee Date Time Place Paper Title / Authors Abstract Paper #
CQ, MVE, IE, IMQ
(Joint) [detail]
2018-03-08
15:30
Okinawa Okinawa Industry Support Center Banner Click Through Rate Classification Using Deep Neural Convolutional Network
Nicolas Michel (Univ. Of Tokyo), Hayato Sakata (SMN), Keita Kurita, Toshihiko Yamasaki (Univ. Of Tokyo) IMQ2017-43 IE2017-135 MVE2017-85
In banner advertising, Click Through Rate (CTR) is one of the most important indicators to evaluate one
advertisement?s... [more]
IMQ2017-43 IE2017-135 MVE2017-85
pp.101-106
IBISML 2017-11-09
13:00
Tokyo Univ. of Tokyo [Poster Presentation] Empirical Bayesian Tree
Masashi Sekino (SMN) IBISML2017-39
We propose a new decision tree learning algorithm ``Empirical Bayesian Tree (EBT)'', which models the outputs of a leaf ... [more] IBISML2017-39
pp.31-38
SWIM 2015-02-27
16:30
Tokyo Kikai-Shinko-Kaikan Bldg. Similarity between Web Sites based on Users' viewing histories -- Comparison of effects of dimension reduction by SVD and ICA --
Yuka Aoki, Kouichi Matsuda (Otsuma Univ.), Akikazu Takeuchi (So-net) SWIM2014-32
This paper reports and discusses similarity between web sites based on user’s viewing histories in Web adverting logs. W... [more] SWIM2014-32
pp.31-36
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