Transductive Inference and Semi-Supervised Learning

Abstract

This chapter discusses the difference between transductive inference and semi-supervised learning. It argues that transductive inference captures the intrinsic properties of the mechanism for extracting additional information from the unla-beled data. It also shows an important role of transduction for creating noninductive models of inference. Let us start with the formal problem setting for transductive inference and semi-supervised learning. and a sequence of k test vectors, find among an admissible set of binary vectors, 1. These remarks were inspired by the discussion, What is the Difference between Trans-ductive Inference and Semi-Supervised Learning?, that took place during a workshop close to Tübingen, Germany (May 24, 2005).