Sequence Learning: Paradigms, Algorithms, and Applications

Ron Sun (auth.), Ron Sun, C. Lee Giles (eds.)3540415971, 9783540415978

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.

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Table of contents :
Introduction to Sequence Learning….Pages 1-10
Sequence Learning via Bayesian Clustering by Dynamics….Pages 11-34
Using Dynamic Time Warping to Bootstrap HMM-Based Clustering of Time Series….Pages 35-52
Anticipation Model for Sequential Learning of Complex Sequences….Pages 53-79
Bidirectional Dynamics for Protein Secondary Structure Prediction….Pages 80-104
Time in Connectionist Models….Pages 105-134
On the Need for a Neural Abstract Machine….Pages 135-161
Sequence Mining in Categorical Domains: Algorithms and Applications….Pages 162-187
Sequence Learning in the ACT-R Cognitive Architecture: Empirical Analysis of a Hybrid Model….Pages 188-212
Sequential Decision Making Based on Direct Search….Pages 213-240
Automatic Segmentation of Sequences through Hierarchical Reinforcement Learning….Pages 241-263
Hidden-Mode Markov Decision Processes for Nonstationary Sequential Decision Making….Pages 264-287
Pricing in Agent Economies Using Neural Networks and Multi-agent Q-Learning….Pages 288-307
Multiple Forward Model Architecture for Sequence Processing….Pages 308-320
Integration of Biologically Inspired Temporal Mechanisms into a Cortical Framework for Sequence Processing….Pages 321-348
Attentive Learning of Sequential Handwriting Movements: A Neural Network Model….Pages 349-387