Sequence Learning

1. Front Matter

   Pages I-XII

   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

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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
• algorithm analysis and problem complexity

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

  Ron Sun

• NEC Research Institute, Princeton, USA

  C. Lee Giles

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