A Learning to Rank framework applied to text-image retrieval
Multimedia Tools and Applications
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We introduce constraint classification, a framework capturing many flavors of multiclass classification including multilabel classification and ranking, and present a meta-algorithm for learning in this framework. We provide generalization bounds when using a collection of $k$ linear functions to represent each hypothesis. We also present empirical and theoretical evidence that constraint classification is more powerful than existing methods of multiclass classification.