Skip to main content

Sequence Learning

Paradigms, Algorithms, and Applications

  • Book
  • © 2001

Overview

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

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

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.

Keywords

Table of contents (16 chapters)

  1. Introduction to Sequence Learning

  2. Sequence Clustering and Learning with Markov Models

  3. Sequence Prediction and Recognition with Neural Networks

  4. Sequence Discovery with Symbolic Methods

  5. Sequential Decision Making

  6. 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