Improving Image Annotation in Imbalanced Classification Problems with Ranking SVM View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010

AUTHORS

Ali Fakeri-Tabrizi , Sabrina Tollari , Nicolas Usunier , Patrick Gallinari

ABSTRACT

We try to overcome the imbalanced data set problem in image annotation by choosing a convenient loss function for learning the classifier. Instead of training a standard SVM, we use a Ranking SVM in which the chosen loss function is helpful in the case of imbalanced data. We compare the Ranking SVM to a classical SVM with different visual features. We observe that Ranking SVM always improves the prediction quality, and can perform up to 23% better than the classical SVM. More... »

PAGES

291-294

References to SciGraph publications

Book

TITLE

Multilingual Information Access Evaluation II. Multimedia Experiments

ISBN

978-3-642-15750-9
978-3-642-15751-6

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-15751-6_37

DOI

http://dx.doi.org/10.1007/978-3-642-15751-6_37

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1030509913


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