Rough Sets and Knowledge Technology

First International Conference, RSKT 2006, Chongquing, China, July 24-26, 2006. Proceedings

  • Guo-Ying Wang
  • James F. Peters
  • Andrzej Skowron
  • Yiyu Yao
Conference proceedings RSKT 2006

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4062)

Also part of the Lecture Notes in Artificial Intelligence book sub series (volume 4062)

Table of contents

  1. Front Matter
  2. Commemorative Paper

    1. James F. Peters, Andrzej Skowron
      Pages 1-11
  3. Keynote Papers

  4. Plenary Papers

  5. Rough Computing

About these proceedings

Introduction

This volume contains the papers selected for presentation at the First Int- national Conference on Rough Sets and Knowledge Technology (RSKT 2006) organized in Chongqing, P. R. China, July 24-26, 2003. There were 503 s- missions for RSKT 2006 except for 1 commemorative paper, 4 keynote papers and 10 plenary papers. Except for the 15 commemorative and invited papers, 101 papers were accepted by RSKT 2006 and are included in this volume. The acceptance rate was only 20%. These papers were divided into 43 regular oral presentation papers (each allotted 8 pages), and 58 short oral presentation - pers (each allotted 6 pages) on the basis of reviewer evaluation. Each paper was reviewed by two to four referees. Since the introduction of rough sets in 1981 by Zdzis law Pawlak, many great advances in both the theory and applications have been introduced. Rough set theory is closely related to knowledge technology in a variety of forms such as knowledge discovery, approximate reasoning, intelligent and multiagent systems design, and knowledge intensive computations that signal the emergence of a knowledge technology age. The essence of growth in cutting-edge, state-of-t- art and promising knowledge technologies is closely related to learning, pattern recognition,machine intelligence and automation of acquisition, transformation, communication, exploration and exploitation of knowledge. A principal thrust of such technologies is the utilization of methodologies that facilitate knowledge processing.

Keywords

Web Intelligence cognition evolution evolutionary computation fuzzy intelligence knowledge learning machine learning pattern recognition

Editors and affiliations

  • Guo-Ying Wang
    • 1
  • James F. Peters
    • 2
  • Andrzej Skowron
    • 3
  • Yiyu Yao
    • 4
  1. 1.College of Computer Science and Technology, ChongqingUniversity of Posts and TelecommunicationChongqingP.R. China
  2. 2.Department of Electrical and Computer EngineeringUniversity of ManitobaWinnipeg, ManitobaCanada
  3. 3.Institute of MathematicsWarsaw UniversityWarsawPoland
  4. 4.Department of Computer ScienceUniversity of Regina, Regina,SaskatchewanCanada

Bibliographic information

  • DOI https://doi.org/10.1007/11795131
  • Copyright Information Springer-Verlag Berlin Heidelberg 2006
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-36297-5
  • Online ISBN 978-3-540-36299-9
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book