© 2001

Sequence Learning

Paradigms, Algorithms, and Applications

  • Ron Sun
  • C. Lee Giles

Part of the Lecture Notes in Computer Science book series (LNCS, volume 1828)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 1828)

Table of contents

  1. Front Matter
    Pages I-XII
  2. Introduction to Sequence Learning

  3. Sequence Clustering and Learning with Markov Models

    1. Paola Sebastiani, Marco Ramoni, Paul Cohen
      Pages 11-34
    2. Tim Oates, Laura Firoiu, Paul R. Cohen
      Pages 35-52
  4. Sequence Prediction and Recognition with Neural Networks

    1. Pierre Baldi, Søren Brunak, Paolo Frasconi, Gianluca Pollastri, Giovanni Soda
      Pages 80-104
    2. Jean-Cédric Chappelier, Marco Gori, Alain Grumbach
      Pages 105-134
    3. Diego Sona, Alessandro Sperduti
      Pages 135-161
  5. Sequence Discovery with Symbolic Methods

  6. Sequential Decision Making

  7. Biologically Inspired Sequence Learning Models

    1. Raju S. Bapi, Kenji Doya
      Pages 308-320
    2. Hervé Frezza-Buet, Nicolas Rougier, Frédéric Alexandre
      Pages 321-348
  8. Back Matter
    Pages 388-389

About this book


Sequential behavior is essential to intelligence in general and a fundamental part of human activities, ranging from reasoning to language, and from everyday skills to complex problem solving. Sequence learning is an important component of learning in many tasks and application fields: planning, reasoning, robotics natural language processing, speech recognition, adaptive control, time series prediction, financial engineering, DNA sequencing, and so on. This book presents coherently integrated chapters by leading authorities and assesses the state of the art in sequence learning by introducing essential models and algorithms and by examining a variety of applications. The book offers topical sections on sequence clustering and learning with Markov models, sequence prediction and recognition with neural networks, sequence discovery with symbolic methods, sequential decision making, biologically inspired sequence learning models.


algorithms behavior biologically inspired cognition control intelligence learning natural language natural language processing neural networks problem solving reinforcement learning robot robotics speech recognition

Editors and affiliations

  • Ron Sun
    • 1
  • C. Lee Giles
    • 2
  1. 1.CECS DepartmentUniversity of Missouri-ColumbiaColumbiaUSA
  2. 2.NEC Research InstitutePrincetonUSA

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