Fusion methods for time-series classification /

Fusion methods for time-series classification /

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Bibliographic Details
Author / Creator:	Buza, Krisztian Antal.
Imprint:	Frankfurt am Main ; New York : Peter Lang, c2011.
Description:	xiii, 144 p. : ill. ; 22 cm.
Language:	English
Series:	Informationstechnologie und Ökonomie, 1616-086x ; Bd. 45 Informationstechnologie und Ökonomie ; Bd. 45.
Subject:	Time-series analysis. Time-series analysis.
Format:	Dissertations Print Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/8682100

Hidden Bibliographic Details
ISBN:	9783631630853 3631630859
Notes:	Originally presented as the author's dissertation--Universität Hildesheim, 2011. Includes bibliographical references (p. [127]-144).
Summary:	"Time-series classification is the common theoretical background of many recognition tasks performed by computers, such as handwriting recognition, speech recognition or detection of abnormalities in electrocardiograph signals. In this book, the state-of-the-art in time-series classification is surveyed and five new techniques are presented. Four out of them aim at making the recognition more accurate, while the proposed instance-selection algorithm speeds up time-series classification. Besides time-series classification tasks, potential applications of the proposed techniques include problems from various domains, e.g. web science or medicine"--Back cover.

Table of Contents:

Individual quality estimation
Instance selection
Fusion of distance measures
The GRAMOFON ensemble framework
Motifs for time-series classification
Outlook: some related applications.