Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
IBISML |
2017-03-07 10:30 |
Tokyo |
Tokyo Institute of Technology |
Doubly Accelerated Stochastic Variance Reduced Gradient Method for Regularized Empirical Risk Minimization Tomoya Murata, Taiji Suzuki (Tokyo Tech) IBISML2016-106 |
We develop a new stochastic gradient method for solving convex regularized empirical risk minimization problem in mini-b... [more] |
IBISML2016-106 pp.49-56 |
VLD |
2016-03-02 13:25 |
Okinawa |
Okinawa Seinen Kaikan |
An Algorithm for Reducing Components of a Gaussian Mixture Model 2
-- A Method for Calculating Sensitivities -- Daiki Azuma, Shuji Tsukiyama (Chuo Univ.), Masahiro Fukui (Ritsumeikan Univ.), Takashi Kambe (Kinki Univ.) VLD2015-139 |
In statistical methods, such as statistical static timing analysis (S-STA), Gaussian mixture model (GMM) is a useful too... [more] |
VLD2015-139 pp.161-166 |
SRW |
2013-10-21 09:30 |
Kanagawa |
NICT@YRP |
Study on Multiple-hop Wireless Body Area Network based on CSMA/CA of IEEE802.15.6 Pham Thanh Hiep (Yokohama National Univ.) SRW2013-28 |
Since the elderly population is increasing all over the world, health care market keeps growing and there is a need for ... [more] |
SRW2013-28 pp.1-6 |
SIS |
2011-03-03 13:50 |
Tokyo |
Tokyo City University (Setagaya Campus) |
Single Image Super-Resolution Using Bilateral Back-Projection Using Local Variance and Inverse Filter Hidenobu Hashikami, Hajime Nobuhara (Univ. of Tsukuba) SIS2010-59 |
Bilateral Back-Projection (BBP) is a single image super resolution method which has a drawback of excessive smoothing ef... [more] |
SIS2010-59 pp.31-36 |
EA |
2010-12-10 13:45 |
Ibaraki |
Univ. of Tsukuba |
Diffuse noise robust multiple source localization based on noise reduction in covariance matrix domain Nobutaka Ito (Univ. Tokyo), Emmanuel Vincent (INRIA Rennes), Nobutaka Ono (Univ. Tokyo), Remi Gribonval (INRIA Rennes), Shigeki Sagayama (Univ. Tokyo) EA2010-102 |
In this paper, we propose a method for estimating the azimuths of multiple sound sources accurately even in the presence... [more] |
EA2010-102 pp.31-36 |
ITE-ME, ITE-AIT, ITE-BCT, IE |
2009-11-19 15:30 |
Fukuoka |
Kyushu Sangyo Univ. |
Noise Reduction for Color CCD Image Sensors Based on Constraint of Mean Color and Local Variance Estimation of Noise and Texture Takayuki Hara, Haike Guan (RICOH) IE2009-105 |
A noise reduction algorithm is proposed for color image sensors like CCD. It has been difficult to reduce color noise at... [more] |
IE2009-105 pp.39-44 |
NC |
2007-10-18 09:30 |
Miyagi |
Tohoku University |
Baysian-optimal image reconstruction for translational-symmetric filter Satohiro Tajima (Univ. Tokyo), Masato Inoue (Waseda Univ.), Masato Okada (Univ. Tokyo) NC2007-33 |
Translational symmetric filter provides a foundation for various image processing. Given that filtered images are observ... [more] |
NC2007-33 pp.1-6 |
SP |
|
Toyama |
Toyama Prefectural University |
Is the ability of identifying a given [a] sound as phoneme /a/ necessary for spoken language competence? Nobuaki Minematsu, Tazuko Nishimura (Univ. of Tokyo), Kyoko Sakuraba (Kiyose Welfare Center), Satoshi Asakawa, Daisuke Saito (Univ. of Tokyo) SP2007-30 |
A language generally has several tens of phonemes. Acoustic substances of the phoneme depend upon its phonemic environme... [more] |
SP2007-30 pp.37-42 |
MoNA, IN (Joint) |
2005-11-18 09:50 |
Fukuoka |
Fukuoka Institute of Technology |
Subspace Based Blind Identification Algorithm using CGM Nari Tanabe (Toyama National College of Tech.), Toshihiro Furukawa (Tokyo University of Science), Shigeo Tsujii (Inst. of Information Security) |
We propose a subspace based blind identification algorithm using CGM (conjugate gradient method). The algorithm estimate... [more] |
MoMuC2005-69 pp.65-68 |
IE, SIP |
2005-04-22 13:25 |
Tokyo |
|
Minimum-Variance Pseudo-Unbiased Low-Rank Estimation
-- A Generalization of Marquardt's Estimator for Ill-Conditioned Inverse Problems -- Jamal Elbadraoui, Isao Yamada (Tokyo Inst. of Tech.) |
This paper presents a novel low-rank linear statistical estimator named minimum-variance pseudo-unbiased low-rank estima... [more] |
SIP2005-6 IE2005-6 pp.31-36 |