Revisiting (\epsilon, \gamma, \tau)-similarity learning for domain adaptation

NeurIPS 2018 Sofiane DhouibIevgen Redko

Similarity learning is an active research area in machine learning that tackles the problem of finding a similarity function tailored to an observable data sample in order to achieve efficient classification. This learning scenario has been generally formalized by the means of a $(\epsilon, \gamma, \tau)-$good similarity learning framework in the context of supervised classification and has been shown to have strong theoretical guarantees... (read more)

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