Sequence Learning : Paradigms, Algorithms, and Applications / edited by Ron Sun, C.Lee Giles.

Format:

Book

Contributor:

Sun, Ron, 1960- editor.

Giles, C. Lee, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 1828.

Lecture Notes in Artificial Intelligence ; 1828

Language:

English

Subjects (All):

Artificial intelligence.

Computers.

Algorithms.

Artificial Intelligence.

Computation by Abstract Devices.

Algorithm Analysis and Problem Complexity.

Local Subjects:

Artificial Intelligence.

Computation by Abstract Devices.

Algorithm Analysis and Problem Complexity.

Physical Description:

1 online resource (XII, 396 pages).

Edition:

First edition 2001.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2001.

System Details:

text file PDF

Summary:

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.

Contents:

to Sequence Learning

to Sequence Learning

Sequence Clustering and Learning with Markov Models

Sequence Learning via Bayesian Clustering by Dynamics

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

Sequence Prediction and Recognition with Neural Networks

Anticipation Model for Sequential Learning of Complex Sequences

Bidirectional Dynamics for Protein Secondary Structure Prediction

Time in Connectionist Models

On the Need for a Neural Abstract Machine

Sequence Discovery with Symbolic Methods

Sequence Mining in Categorical Domains: Algorithms and Applications

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

Sequential Decision Making

Sequential Decision Making Based on Direct Search

Automatic Segmentation of Sequences through Hierarchical Reinforcement Learning

Hidden-Mode Markov Decision Processes for Nonstationary Sequential Decision Making

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

Biologically Inspired Sequence Learning Models

Multiple Forward Model Architecture for Sequence Processing

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

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

Other Format:

Printed edition:

ISBN:

978-3-540-44565-4

9783540445654

Access Restriction:

Restricted for use by site license.