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Reliable Knowledge Discovery

Editors: Dai, Honghua, Liu, James N. K., Smirnov, Evgueni (Eds.)

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eBook $229.00
price for USA
  • ISBN 978-1-4614-1903-7
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Download immediately after purchase
Hardcover $299.00
price for USA
  • ISBN 978-1-4614-1902-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $299.00
price for USA
  • ISBN 978-1-4899-9532-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

Reliable Knowledge Discovery focuses on theory, methods, and techniques for RKDD, a new sub-field of KDD. It studies the theory and methods to assure the reliability and trustworthiness of discovered knowledge and to maintain the stability and consistency of knowledge discovery processes. RKDD has a broad spectrum of applications, especially in critical domains like medicine, finance, and military.

Reliable Knowledge Discovery also presents methods and techniques for designing robust knowledge-discovery processes. Approaches to assessing the reliability of the discovered knowledge are introduced. Particular attention is paid to methods for reliable feature selection, reliable graph discovery, reliable classification, and stream mining. Estimating the data trustworthiness is covered in this volume as well. Case studies are provided in many chapters.

Reliable Knowledge Discovery is designed for researchers and advanced-level students focused on computer science and electrical engineering as a secondary text or reference. Professionals working in this related field and KDD application developers will also find this book useful.

Table of contents (17 chapters)

  • Transductive Reliability Estimation for Individual Classifications in Machine Learning and Data Mining

    Kukar, Matjaž

    Pages 3-27

  • Estimating Reliability for Assessing and Correcting Individual Streaming Predictions

    Rodrigues, Pedro Pereira (et al.)

    Pages 29-49

  • Error Bars for Polynomial Neural Networks

    Nikolaev, Nikolay (et al.)

    Pages 51-66

  • Robust-Diagnostic Regression: A Prelude for Inducing Reliable Knowledge from Regression

    Nurunnabi, Abdul Awal Md. (et al.)

    Pages 69-92

  • Reliable Graph Discovery

    Dai, Honghua

    Pages 93-107

Buy this book

eBook $229.00
price for USA
  • ISBN 978-1-4614-1903-7
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Download immediately after purchase
Hardcover $299.00
price for USA
  • ISBN 978-1-4614-1902-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $299.00
price for USA
  • ISBN 978-1-4899-9532-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Reliable Knowledge Discovery
Editors
  • Honghua Dai
  • James N. K. Liu
  • Evgueni Smirnov
Copyright
2012
Publisher
Springer-Verlag New York
Copyright Holder
Springer Science+Business Media, LLC
eBook ISBN
978-1-4614-1903-7
DOI
10.1007/978-1-4614-1903-7
Hardcover ISBN
978-1-4614-1902-0
Softcover ISBN
978-1-4899-9532-2
Edition Number
1
Number of Pages
XVIII, 310
Topics