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On the Usefulness of Similarity based Projection Spaces for Transfer Learning

On the Usefulness of Similarity based Projection Spaces for Transfer Learning

This video was recorded at 1st International Workshop on Similarity-Based Pattern Analysis and Recognition. Similarity functions are widely used in many machine learning or pattern recognition tasks. We consider here a recent framework for binary classification, proposed by Balcan et al., allowing to learn in a potentially non geometrical space based on good similarity functions. This framework is a generalization of the notion of kernels used in support vector machines in the sense that allows one to use similarity functions that do not need to be positive semi-definite nor symmetric. The similarities are then used to define an explicit projection space where a linear classifier with good generalization properties can be learned. In this paper, we propose to study experimentally the... Show More
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