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	Author	Jaume Gibert; Ernest Valveny; Oriol Ramos Terrades; Horst Bunke
	Title	Multiple Classifiers for Graph of Words Embedding			Type	Conference Article
	Year	2011	Publication	10th International Conference on Multiple Classifier Systems	Abbreviated Journal
	Volume	6713	Issue		Pages	36-45
	Keywords
	Abstract	During the last years, there has been an increasing interest in applying the multiple classifier framework to the domain of structural pattern recognition. Constructing base classifiers when the input patterns are graph based representations is not an easy problem. In this work, we make use of the graph embedding methodology in order to construct different feature vector representations for graphs. The graph of words embedding assigns a feature vector to every graph by counting unary and binary relations between node representatives and combining these pieces of information into a single vector. Selecting different node representatives leads to different vectorial representations and therefore to different base classifiers that can be combined. We experimentally show how this methodology significantly improves the classification of graphs with respect to single base classifiers.
	Address	Napoles, Italy
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor	Carlo Sansone; Josef Kittler; Fabio Roli
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-3-642-21556-8	Medium
	Area		Expedition		Conference	MCS
	Notes	DAG			Approved	no
	Call Number	Admin @ si @GVR2011			Serial	1745
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