Machine Learning and Cybernetics

13th International Conference, Lanzhou, China, July 13-16, 2014. Proceedings

  • Xizhao Wang
  • Witold Pedrycz
  • Patrick Chan
  • Qiang He
Conference proceedings ICMLC 2014

Part of the Communications in Computer and Information Science book series (CCIS, volume 481)

Table of contents

  1. Front Matter
    Pages I-XIV
  2. Classification and Semi-Supervised Learning

    1. Front Matter
      Pages 1-1
    2. Tien Thanh Nguyen, Alan Wee-Chung Liew, Minh Toan Tran, Mai Phuong Nguyen
      Pages 3-12
    3. Qiangzhi Zhang, Huali Chang, Longzhong Liu, Anhua Li, Qinghua Huang
      Pages 24-32
    4. Donghui Wang, Junhai Zhai, Hong Zhu, Xizhao Wang
      Pages 33-42
    5. Aixia Chen, Huimin Feng, Zhen Guo
      Pages 43-51
    6. Yongshuai Hou, Xiaolong Wang, Qingcai Chen, Man Li, Cong Tan
      Pages 52-59
    7. Tien Thanh Nguyen, Alan Wee-Chung Liew, Minh Toan Tran, Thi Thu Thuy Nguyen, Mai Phuong Nguyen
      Pages 60-68
  3. Clustering and Kernel

    1. Front Matter
      Pages 69-69
    2. Cuong To, Tien Thanh Nguyen, Alan Wee-Chung Liew
      Pages 97-106
    3. Huixin Xu, Yun Xue, Zhihao Lu, Xiaohui Hu, Hongya Zhao, Zhengling Liao et al.
      Pages 107-116
    4. Ziqian Zeng, Yueming Lv, Wing W. Y. Ng
      Pages 117-126
  4. Application to Recognition

    1. Front Matter
      Pages 127-127
    2. Mohammad M. Rahman, Bruce Poon, M. Ashraful Amin, Hong Yan
      Pages 129-135
    3. Qin Qing, Eric C. C. Tsang
      Pages 136-143
    4. Patrick P. K. Chan, Ying Shu
      Pages 144-150

About these proceedings

Introduction

This book constitutes the refereed proceedings of the 13th International Conference on Machine Learning and Cybernetics, Lanzhou, China, in July 2014. The 45 revised full papers presented were carefully reviewed and selected from 421 submissions. The papers are organized in topical sections on classification and semi-supervised learning; clustering and kernel; application to recognition; sampling and big data; application to detection; decision tree learning; learning and adaptation; similarity and decision making; learning with uncertainty; improved learning algorithms and applications.

Keywords

classifier decision making kernel methods machine learning applications machine learning theory reinforcement methods structured prediction support vector machines

Editors and affiliations

  • Xizhao Wang
    • 1
  • Witold Pedrycz
    • 2
  • Patrick Chan
    • 3
  • Qiang He
    • 4
  1. 1.Hebei UniversityBaodingChina
  2. 2.Department of Electrical and Computer EnUniversity of AlbertaEdmontonCanada
  3. 3.South China University of TechnologyGuangzhouChina
  4. 4.Hebei UniversityBaodingChina

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-662-45652-1
  • Copyright Information Springer-Verlag Berlin Heidelberg 2014
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-662-45651-4
  • Online ISBN 978-3-662-45652-1
  • Series Print ISSN 1865-0929
  • Series Online ISSN 1865-0937
  • About this book
Industry Sectors
Pharma
Automotive
Chemical Manufacturing
Biotechnology
Electronics
Telecommunications
Energy, Utilities & Environment
Aerospace