没有合适的资源?快使用搜索试试~ 我知道了~
Information theory and statistical learning
5星 · 超过95%的资源 需积分: 9 9 下载量 32 浏览量
2009-09-18
14:15:45
上传
评论 1
收藏 6.7MB PDF 举报
温馨提示
试读
442页
Frank Emmert-Streib, Matthias Dehmer. Springer, 2009.
资源推荐
资源详情
资源评论
Information Theory and Statistical Learning
Frank Emmert-Streib
•
Matthias Dehmer
Information Theory
and Statistical Learning
ABC
Frank Emmert-Streib
University of Washington
Department of Biostatistics
and Department of Genome Sciences
1705 NE Pacific St.,
Box 357730
Seattle WA 98195, USA
and
Queen’s University Belfast
Computational Biology
and Machine Learning
Center for Cancer Research
and Cell Biology
School of Biomedical Sciences
97 Lisburn Road, Belfast BT9 7BL, UK
v@bio-complexity.com
Matthias Dehmer
Vienna University of Technology
Institute of Discrete Mathematics
and Geometry
Wiedner Hauptstr. 8–10
1040 Vienna, Austria
and
University of Coimbra
Center for Mathematics
Probability and Statistics
Apartado 3008, 3001–454
Coimbra, Portugal
matthias@dehmer.org
ISBN: 978-0-387-84815-0 e-ISBN: 978-0-387-84816-7
DOI: 10.1007/978-0-387-84816-7
Library of Congress Control Number: 2008932107
c
Springer Science+Business Media, LLC 2009
All rights reserved. This work may not be translated or copied in whole or in part without the written
permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York,
NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in
connection with any form of information storage and retrieval, electronic adaptation, computer software,
or by similar or dissimilar methodology now known or hereafter developed is forbidden.
The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are
not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject
to proprietary rights.
Printed on acid-free paper
springer.com
Preface
This book presents theoretical and practical results of information theoretic methods
used in the context of statistical learning. Its major goal is to advocate and promote
the importance and usefulness of information theoretic concepts for understanding
and developing the sophisticated machine learning methods necessary not only to
cope with the challenges of modern data analysis but also to gain further insights
into their theoretical foundations. Here Statistical Learning is loosely defined as a
synonym, for, e.g., Applied Statistics, Artificial Intelligence or Machine Learning.
Over the last decades, many approaches and algorithms have been suggested in the
fields mentioned above, for which information theoretic concepts constitute core
ingredients. For this reason we present a selected collection of some of the finest
concepts and applications thereof from the perspective of information theory as the
underlying guiding principles. We consider such a perspective as very insightful and
expect an even greater appreciation for this perspective over the next years.
The book is intended for interdisciplinary use, ranging from Applied Statistics,
Artificial Intelligence, Applied Discrete Mathematics, Computer Science, Infor-
mation Theory, Machine Learning to Physics. In addition, people working in the
hybrid fields of Bioinformatics, Biostatistics, Computational Biology, Computa-
tional Linguistics, Medical Bioinformatics, Neuroinformatics or Web Mining might
profit tremendously from the presented results because these data-driven areas are
in permanent need of new approaches to cope with the increasing flood of high-
dimensional, noisy data that possess seemingly never ending challenges for their
analysis.
Many colleagues, whether consciously or unconsciously, have provided us with
input, help and support before and during the writing of this book. In particular we
would like to thank Shun-ichi Amari, Hamid Arabnia, G
¨
okhan Bakır, Alexandru T.
Balaban, Teodor Silviu Balaban, Frank J. Balbach, Jo
˜
ao Barros, Igor Bass, Matthias
Beck, Danail Bonchev, Stefan Borgert, Mieczyslaw Borowiecki, Rudi L. Cilibrasi,
Mike Coleman, Malcolm Cook, Pham Dinh-Tuan, Michael Drmota, Shinto Eguchi,
B. Roy Frieden, Bernhard Gittenberger, Galina Glazko, Martin Grabner, Earl
Glynn, Peter Grassberger, Peter Hamilton, Kate
ˇ
rina Hlav
´
a
ˇ
ckov
´
a-Schindler, Lucas
R. Hope, Jinjie Huang, Robert Jenssen, Attila Kert
´
esz-Farkas, Andr
´
as Kocsor,
v
vi Preface
Elena Konstantinova, Kevin B. Korb, Alexander Kraskov, Tyll Kr
¨
uger, Ming Li, J.F.
McCann, Alexander Mehler, Marco M
¨
oller, Abbe Mowshowitz, Max M
¨
uhlh
¨
auser,
Markus M
¨
uller, Noboru Murata, Arcady Mushegian, Erik P. Nyberg, Paulo Eduardo
Oliveira, Hyeyoung Park, Judea Pearl, Daniel Polani, S
´
andor Pongor, William
Reeves, Jorma Rissanen, Panxiang Rong, Reuven Rubinstein, Rainer Siegmund
Schulze, Heinz Georg Schuster, Helmut Schwegler, Chris Seidel, Fred Sobik, Ray
J. Solomonoff, Doru Stefanescu, Thomas Stoll, John Storey, Milan Studeny, Ulrich
Tamm, Naftali Tishby, Paul M.B. Vit
´
anyi, Jos
´
e Miguel Urbano, Kazuho Watanabe,
Dongxiao Zhu, Vadim Zverovich and apologize to all those who have been missed
inadvertently. We would like also to thank our editor Amy Brais from Springer who
has always been available and helpful. Last but not least we would like to thank our
families for support and encouragement during all the time of preparing the book
for publication.
We hope this book will help to spread the enthusiasm we have for this field and
inspire people to tackle their own practical or theoretical research problems.
Belfast and Coimbra Frank Emmert-Streib
June 2008 Matthias Dehmer
剩余441页未读,继续阅读
资源评论
- idiotwei20142018-08-24亚马逊看到的好书,下来看看,效果很好,感谢分享
westower
- 粉丝: 1
- 资源: 7
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功