Sequence Learning : Paradigms, Algorithms, and Applications - Ron Sun

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Sequence Learning

Paradigms, Algorithms, and Applications

By: Ron Sun (Editor), C.Lee Giles (Editor)

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Paperback | 10 January 2001

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412 Pages

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22.86 x 15.88 x 2.54

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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.

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Introduction to Sequence Learning	p. 1
Sequence Clustering and Learning with Markov Models
Sequence Learning via Bayesian Clustering by Dynamics	p. 11
Using Dynamic Time Warping to Bootstrap HMM-Based Clustering of Time Series	p. 35
Sequence Prediction and Recognition with Neural Networks
Anticipation Model for Sequential Learning of Complex Sequences	p. 53
Bidirectional Dynamics for Protein Secondary Structure Prediction	p. 80
Time in Connectionist Models	p. 105
On the Need for a Neural Abstract Machine	p. 135
Sequence Discovery with Symbolic Methods
Sequence Mining in Categorical Domains: Algorithms and Applications	p. 162
Sequence Learning in the ACT-R Cognitive Architecture: Empirical Analysis of a Hybrid Model	p. 188
Sequential Decision Making
Sequential Decision Making Based on Direct Search	p. 213
Automatic Segmentation of Sequences through Hierarchical Reinforcement Learning	p. 241
Hidden-Mode Markov Decision Processes for Nonstationary Sequential Decision Making	p. 264
Pricing in Agent Economies Using Neural Networks and Multi-agent Q-learning	p. 288
Biologically Inspired Sequence Learning Models
Multiple Forward Model Architecture for Sequence Processing	p. 308
Integration of Biologically Inspired Temporal Mechanisms into a Cortical Framework for Sequence Processing	p. 321
Attentive Learning of Sequential Handwriting Movements: A Neural Network Model	p. 349
About Editors	p. 389
Author Index	p. 391
Table of Contents provided by Publisher. All Rights Reserved.

Sequence Learning

Paradigms, Algorithms, and Applications

At a Glance

Paperback

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How to return your order

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