Skip to main content
SpringerLink
Log in

Sequence Learning

Paradigms, Algorithms, and Applications

  • Book
  • © 2001

Overview

Editors:
  1. Ron Sun

    1. CECS Department, University of Missouri-Columbia, Columbia, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. C. Lee Giles

    1. NEC Research Institute, Princeton, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

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

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (16 chapters)

  1. Front Matter

    Pages I-XII
    Download chapter PDF

  2. Introduction to Sequence Learning

    1. Introduction to Sequence Learning

      • Ron Sun
      Pages 1-10

  3. Sequence Clustering and Learning with Markov Models

    1. Sequence Learning via Bayesian Clustering by Dynamics

      • Paola Sebastiani, Marco Ramoni, Paul Cohen
      Pages 11-34

    2. Using Dynamic Time Warping to Bootstrap HMM-Based Clustering of Time Series

      • Tim Oates, Laura Firoiu, Paul R. Cohen
      Pages 35-52

  4. Sequence Prediction and Recognition with Neural Networks

    1. Anticipation Model for Sequential Learning of Complex Sequences

      • DeLiang Wang
      Pages 53-79

    2. Bidirectional Dynamics for Protein Secondary Structure Prediction

      • Pierre Baldi, Søren Brunak, Paolo Frasconi, Gianluca Pollastri, Giovanni Soda
      Pages 80-104

    3. Time in Connectionist Models

      • Jean-Cédric Chappelier, Marco Gori, Alain Grumbach
      Pages 105-134

    4. On the Need for a Neural Abstract Machine

      • Diego Sona, Alessandro Sperduti
      Pages 135-161

  5. Sequence Discovery with Symbolic Methods

    1. Sequence Mining in Categorical Domains: Algorithms and Applications

      • Mohammed J. Zaki
      Pages 162-187

    2. Sequence Learning in the ACT-R Cognitive Architecture: Empirical Analysis of a Hybrid Model

      • Christian Lebiere, Dieter Wallach
      Pages 188-212

  6. Sequential Decision Making

    1. Sequential Decision Making Based on Direct Search

      • Jürgen Schmidhuber
      Pages 213-240

    2. Automatic Segmentation of Sequences through Hierarchical Reinforcement Learning

      • R. Sun, C. Sessions
      Pages 241-263

    3. Hidden-Mode Markov Decision Processes for Nonstationary Sequential Decision Making

      • Samuel P. M. Choi, Dit-Yan Yeung, Nevin L. Zhang
      Pages 264-287

    4. Pricing in Agent Economies Using Neural Networks and Multi-agent Q-Learning

      • Gerald Tesauro
      Pages 288-307

  7. Biologically Inspired Sequence Learning Models

    1. Multiple Forward Model Architecture for Sequence Processing

      • Raju S. Bapi, Kenji Doya
      Pages 308-320

    2. Integration of Biologically Inspired Temporal Mechanisms into a Cortical Framework for Sequence Processing

      • Hervé Frezza-Buet, Nicolas Rougier, Frédéric Alexandre
      Pages 321-348

    3. Attentive Learning of Sequential Handwriting Movements: A Neural Network Model

      • Stephen Grossberg, Rainer W. Paine
      Pages 349-387

  8. Back Matter

    Pages 388-389
    Download chapter PDF
Back to top

Keywords

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.

Editors and Affiliations

  • CECS Department, University of Missouri-Columbia, Columbia, USA

    Ron Sun

  • NEC Research Institute, Princeton, USA

    C. Lee Giles

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation