IEEE Signal Processing Society Japan Chapter 会員各位

     IEEE Signal Processing Society Japan Chapter
             Chair      杉山昭彦(NEC)
             Vice Chair 嵯峨山茂樹(東京大学)

IEEE Signal Processing Society Japan Chapter の主催で下記の
Prof. Sergios Theodoridis(アテネ大、IEEE Fellow, Distinguished
Lecturer, 前Eurasip会長)の特別講演会を開催いたします。奮ってご
参加いただきますようお願いいたします。

              記

          IEEE SPS Japan Chapter 特別講演会

(1) 講演者: Prof. Sergios Theodoridis
            (University of Athens, Greece)

(2) 開催日時:2009年6月4日(木) 11:00-12:00

(3) 会  場:東工大南3号館201号室:電気情報系第1会議室
              http://www.titech.ac.jp/about/campus/index.html
       http://www.titech.ac.jp/about/campus/o_map.html

  世話人:山田功 (東京工業大学)

(4) 講演題目
Adaptive Learning in a World of Projections

(5) 講演概要
The task of parameter/function estimation has been at the center
of scientific attention for a long time and it comes under different
names such as filtering, prediction, beamforming, curve fitting,
classification, regression.

In this talk, the estimation task is treated in the context of set
theoretic estimation arguments. Instead of a single optimal point,
we are searching for a set of solutions that are in agreement with
the available information, which is provided to us in the form of a
set of training points and a set of constraints. Each point in the
training data set, as well as each one of the constraints, is
associated with a convex set, constructed according to a (convex)
loss function (differentiable or not).

The goal of this talk is to present a general tool for
parameter/function estimation, under a set of convex constraints,
both for classification as well as regression tasks, in a time
adaptive setting in (infinite dimensional) Reproducing Kernel
Hilbert spaces (RKHS).

The algorithmic scheme consists of a sequence of projections, of
linear complexity with respect to the number of unknown parameters.
Our theory proves that such a scheme converges to the intersection
of all (with the possible exception of a finite number of) the
convex sets, where the required solution lies. The performance of
the methodology is demonstrated in the context of nonlinear
classification and robust beamforming in communication systems.

The work has been carried out in cooperation with Konstantinos
Slavakis and Isao Yamada.

(6) 講演者紹介
Sergios Theodoridis is currently Professor of Signal Processing
and Communications in the Department of Informatics and
Telecommunications of the University of Athens. His research
interests lie in the areas of Adaptive Algorithms and Communications,
Machine Learning and Pattern Recognition, Signal Processing for Audio
Processing and Retrieval. He is the co-editor of the book "Efficient
Algorithms for Signal Processing and System Identification",
Prentice Hall 1993, the co-author of the book "Pattern Recognition",
Academic Press, 4th Ed. 2008, and the co-author of three books in
Greek, two of them for the Greek Open University.

He is the co-author of four papers that have received best paper
awards, including the IEEE Computational Intelligence Society
Transactions on Neural Networks Outstanding Paper Award. He currently
serves as Distinguished Lecturer of the IEEE Signal Processing 
Society.

He has served as President of EURASIP and he is currently a member of
the Board of Governors for the IEEE CAS Society.

He is a member of the Greek National Council for Research and
Technology and Chairman of the SP advisory committee for the Edinburgh
Research Partnership (ERP). He has served as vice chairman of the 
Greek
Pedagogical Institute and he was for four years member of the Board of
Directors of COSMOTE (the Greek mobile phone operating company). He is
Fellow of IET, a Corresponding Fellow of RSE and a Fellow of IEEE.

       問い合わせ先: 梶川嘉延 (Secretary)
            564-8680 吹田市山手町3-3-35
            関西大学 システム理工学部 電気電子情報工学科
            Tel: 06-6368-1121    Fax: 06-6330-3770
            E-mail : kaji  kansai-u.ac.jp