Result: A nonparametric classifier for unsegmented text
Title:
A nonparametric classifier for unsegmented text
Authors:
Source:
Document recognition and retrieval XI (San Jose CA, 21-22 January 2004)SPIE proceedings series. 5296:102-108
Publisher Information:
Bellingham WA: SPIE, 2004.
Publication Year:
2004
Physical Description:
print, 12 ref
Original Material:
INIST-CNRS
Subject Terms:
Documentation, Electronics, Electronique, Optics, Optique, Physics, Physique, Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Sciences de l'information. Documentation, Information science. Documentation, Systèmes de recherche d'informations. Système de gestion documentaire et d'information, Information retrieval systems. Information and document management system, Interfaces. Logiciels, Interfaces. Software, Sciences de l'information et de la communication, Information and communication sciences, Système de recherche documentaire. Système de gestion documentaire et d'information, Classificateur, Classifier, Clasificador, En ligne, On line, En línea, Méthode non paramétrique, Non parametric method, Método no paramétrico, Numérisation, Digitizing, Numerización, Recherche information, Information retrieval, Búsqueda información, Texte, Text, Texto, Document numérisé, Digitized document, Ecriture, Writing
Document Type:
Conference
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Rensselaer Polytechnic Institute, Troy, NY 12180-3590, United States
University of Nebraska, Lincoln, NE, 68588-0115, United States
Lehigh University, Bethlehem, PA, 18015-3084, United States
Indian Institute of Technology, Kanpur, 208 016, India
University of Nebraska, Lincoln, NE, 68588-0115, United States
Lehigh University, Bethlehem, PA, 18015-3084, United States
Indian Institute of Technology, Kanpur, 208 016, India
Rights:
Copyright 2004 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Sciences of information and communication. Documentation
FRANCIS
FRANCIS
Accession Number:
edscal.16075819
Database:
PASCAL Archive
Further Information
Symbolic Indirect Correlation (SIC) is a new classification method for unsegmented patterns. SIC requires two levels of comparisons. First, the feature sequences from an unknown query signal and a known multi-pattern reference signal are matched. Then, the order of the matched features is compared with the order of matches between every lexicon symbol-string and the reference string in the lexical domain. The query is classified according to the best matching lexicon string in the second comparison. Accuracy increases as classified feature-and-symbol strings are added to the reference string.