Transforming strings to vector spaces using prototype selection

B Spillmann, M Neuhaus, H Bunke, EM Pekalska, RPW Duin

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

56 Citations (Scopus)


A common way of expressing string similarity in structural pattern recognition is the edit distance. It allows one to apply the kNN rule in order to classify a set of strings. However, compared to the wide range of elaborated classi¿ers known from statistical pattern recognition, this is only a very basic method. In the present paper we propose a method for transforming strings into n-dimensional real vector spaces based on prototype selection. This allows us to subsequently classify the transformed strings with more sophisticated classi¿ers, such as support vector machine and other kernel based methods. In a number of experiments, we show that the recognition rate can be signi¿cantly improved by means of this procedure.
Original languageUndefined/Unknown
Title of host publicationStructural, syntactic and statistical pattern recognition
EditorsDY Yeung, JT Kwok, A Fred, F Roli, D de Ridder
Place of PublicationBerlin-Heidelberg
Number of pages10
ISBN (Print)3-540-37236-9
Publication statusPublished - 2006
EventJoint IAPR International Workshops SSPR 2006 and SPR 2006, Hong Kong, China - Heidelberg
Duration: 17 Aug 200619 Aug 2006

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


ConferenceJoint IAPR International Workshops SSPR 2006 and SPR 2006, Hong Kong, China


  • Wiskunde en Informatica
  • Techniek
  • technische Wiskunde en Informatica
  • conference contrib. refereed
  • CWTS 0.75 <= JFIS < 2.00

Cite this