Machine Learning and Its Applications

Advanced Lectures

  • Georgios Paliouras
  • Vangelis Karkaletsis
  • Constantine D. Spyropoulos
Textbook ACAI 1999

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2049)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 2049)

Table of contents

  1. Front Matter
    Pages I-VIII
  2. Methods

    1. Ryszard S. Michalski, Kenneth A. Kaufman
      Pages 22-38
    2. Blaž Zupan, Ivan Bratko, Marko Bohanec, Janez Demšar
      Pages 71-101
    3. Ramon Lopez de Mantaras
      Pages 127-145
    4. Jonathan Shapiro
      Pages 146-168
    5. Sergios Theodoridis, Konstantinos Koutroumbas
      Pages 169-195
    6. Lorenza Saitta
      Pages 218-229
    7. Theodoros Evgeniou, Massimiliano Pontil
      Pages 249-257
    8. Nikos D. Fakotakis, Kyriakos N. Sgarbas
      Pages 267-273
    9. Grigoris Karakoulas, Giovanni Semeraro
      Pages 274-280
    10. Themis Panayiotopoulos, Nick Z. Zacharis
      Pages 281-285
    11. Christos Papatheodorou
      Pages 286-294
    12. Hans C. Jessen, Georgios Paliouras
      Pages 295-299
    13. George D. Magoulas, Andriana Prentza
      Pages 300-307
    14. Nikolaos Hatziargyriou
      Pages 308-317
    15. Nikolaos Vassilas, Elias Kalapanidas, Nikolaos Avouris, Stavros Perantonis
      Pages 318-324
  3. Back Matter
    Pages 325-325

About this book


In recent years machine learning has made its way from artificial intelligence into areas of administration, commerce, and industry. Data mining is perhaps the most widely known demonstration of this migration, complemented by less publicized applications of machine learning like adaptive systems in industry, financial prediction, medical diagnosis and the construction of user profiles for Web browsers.
This book presents the capabilities of machine learning methods and ideas on how these methods could be used to solve real-world problems. The first ten chapters assess the current state of the art of machine learning, from symbolic concept learning and conceptual clustering to case-based reasoning, neural networks, and genetic algorithms. The second part introduces the reader to innovative applications of ML techniques in fields such as data mining, knowledge discovery, human language technology, user modeling, data analysis, discovery science, agent technology, finance, etc.


Algorithmic Learning Case-Based Reasoning Clustering Data Mining Inference Support Vector Machine User Modeling algorithms artificial intelligence cognition genetic algorithms intelligence learning machine learning supervised learning

Editors and affiliations

  • Georgios Paliouras
    • 1
  • Vangelis Karkaletsis
    • 1
  • Constantine D. Spyropoulos
    • 1
  1. 1.National Centre for Scientific Research “Demokritos”Institute of Informatics and TelecommunicationsAthensGreece

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2001
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-42490-1
  • Online ISBN 978-3-540-44673-6
  • Series Print ISSN 0302-9743
  • Buy this book on publisher's site
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