Treffer: Structured output prediction with support vector machines
Title:
Structured output prediction with support vector machines
Authors:
Source:
Structural, syntactic, and statistical pattern recognition (joint IAPR international workshops, SSPR 2006 and SPR 2006, Hong Kong, China, August 17-19, 2006)0SSPR 2006. :1-7
Publisher Information:
Berlin: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 23 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Intelligence artificielle, Artificial intelligence, Reconnaissance et synthèse de la parole et du son. Linguistique, Speech and sound recognition and synthesis. Linguistics, Alignement, Alignment, Alineamiento, Analyse statistique, Statistical analysis, Análisis estadístico, Analyse structurale, Structural analysis, Análisis estructural, Analyse syntaxique, Syntactic analysis, Análisis sintáxico, Apprentissage probabilités, Probability learning, Aprendizaje probabilidades, Apprentissage supervisé, Supervised learning, Aprendizaje supervisado, Délai d'exécution, Time allowed, Plazo ejecución, Langage naturel, Natural language, Lenguaje natural, Machine exemple support, Vector support machine, Máquina ejemplo soporte, Méthode arborescente, Tree structured method, Método arborescente, Reconnaissance forme, Pattern recognition, Reconocimiento patrón, Relation équivalence, Equivalence relation, Relación equivalencia, Temps polynomial, Polynomial time, Tiempo polinomial
Document Type:
Konferenz
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Cornell University, Ithaca, NY, United States
ISSN:
0302-9743
Rights:
Copyright 2007 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:
Computer science; theoretical automation; systems
Accession Number:
edscal.19151950
Database:
PASCAL Archive
Weitere Informationen
This abstract accompanying a presentation at S+SSPR 2006 explores the use of Support Vector Machines (SVMs) for predicting structured objects like trees, equivalence relations, or alignments. It is shown that SVMs can be extended to these problems in a well-founded way, still leading to a convex quadratic training problem and maintaining the ability to use kernels. While the training problem has exponential size, there is a simple algorithm that allows training in polynomial time. The algorithm is implemented in the SVM-Struct software, and it is discussed how the approach can be applied to problems ranging from natural language parsing to supervised clustering.