Transductive learning is an alternative learning model pioneered by Vapnik almost 30 years ago. Our goal in transduction is to transduce information from a given labeled set to a given unlabeled test set so as to label the test points as accurately as possible. Empirical studies indicate that in some applications transduction allows for significantly more accurate predictions than the standard supervised learning inductive approach. Despite the conceptual simplicity of this model and the growing attention it receives, our understanding of transduction is still quite limited. This talk will review the current state of transductive learning with emphasis on recent performance guarantees, learning principles and open questions.
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