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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| RCS, CCS, SR, SRW (Joint) |
2016-03-04 16:20 |
Tokyo | Tokyo Institute of Technology | A Study on Location Estimation Method by Wi-SUN Using Machine Learning Hiroshi Sakamoto, Hiroyuki Yasuda, Thong Huynh, Kaori Kuroda (Tokyo Univ. of Science), Yozo Shoji (NICT), Mikio Hasegawa (Tokyo Univ. of Science)  CCS2015-78 |
Wi-SUN is a wireless communication standard that has been developed as communication scheme for smart meter to record in... [more] | CCS2015-78 pp.63-66 |
| IBISML | 2015-11-26 15:00 |
Ibaraki | Epochal Tsukuba | [Poster Presentation]
Robustification of Learning Algorithms using Hinge-loss Takafumi Kanamori (Nagoya Univ.), Shuhei Fujiwara (TopGate), Akiko Takeda (Univ. of Tokyo) IBISML2015-71 |
We propose a unified formation of robust learning methods for classification and regression problems. In the learnin... [more] |
IBISML2015-71 pp.139-146 |
| MBE | 2009-05-22 11:10 |
Toyama | Toyama Univ. | An Attempt of a Novel Calibration Method for Pulse Oximetry Using Support Vector Machines Non-Linear Regression Hirotaka Nomoto, Mitsuhiro Ogawa (Kanawaza Univ), Yasuhiro Yamakoshi (yu.sys Corp.), Masamichi Nogawa, Takehiro Yamakoshi, Kosuke Motoi, Shinobu Tanaka, Ken-ichi Yamakoshi (Kanawaza Univ) MBE2009-2 |
A new calibration method using a non linear multivariate regression method, support vector machines regression (SVMsR) o... [more] | MBE2009-2 pp.5-8 |
| NC, MBE (Joint) |
2008-12-20 14:30 |
Aichi | Nagoya Inst. Tech. | Gradient Based Two Dimensional Path Following for Kernel Machines Masayuki Karasuyama, Ichiro Takeuchi (NIT), Ryohei Nakano (Chubu Univ.) NC2008-80 |
The performance of the Kernel Machines depends on its hyperparameters such as a regularization parameter. Since the pro... [more] |
NC2008-80 pp.43-48 |
| MBE, NC (Joint) |
2007-12-22 09:50 |
Aichi | Optimizing SVR Hyperparameters via Fast Cross-Validation Masayuki Karasuyama, Ryohei Nakano (Nagoya Inst. of Tech.) NC2007-73 |
The performance of Support Vector Regression (SVR) deeply depends on its hyperparameters such as an insensitive zone thi... [more] | NC2007-73 pp.13-18 |
|
| NC | 2007-03-14 11:20 |
Tokyo | Tamagawa University | On Variable Selection in Decomposition Methods for Support Vector Machines
-- Proposal and Experimental Evaluation of a Novel Variable Selection based on Conjugate Gradient Method -- Yusuke Kawazoe (Kyushu Univ.), Masashi Kuranoshita (FUJIFILM), Norikazu Takahashi, Jun'ichi Takeuchi (Kyushu Univ.) |
Learning of a support vector machine (SVM) is formulated as a quadratic programming (QP) problem. Decomposition method... [more] |
NC2006-139 pp.127-132 |
| NLP | 2005-11-19 15:40 |
Fukuoka | Kyushu Institute of Technology | Application of minimum description length to Least Squares Support Vector Machines for modeling chaotic dynamical systems Tsutomu Maeda, Masaharu Adachi (Tokyo Denki Univ.) |
In this study, we attempt to prune the support vectors of Least Squares Support Vector Machines for function estimation... [more] | NLP2005-83 pp.71-76 |
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