# Transductive Conformal Predictor

In basic conformal prediction the goal is to predict the label of a test object {`⚠ $x_{n+1}$`

} given a training set {`⚠ $z_1,\ldots,z_n$`

}. In transductive conformal prediction, we are given a set {`⚠ $x_{n+1},\ldots,x_{n+k}$`

} of test objects and the goal is to predict their labels without getting any intermediate feedback. This is motivated by the general problem of transduction in machine learning.

**References**

- Vladimir Vapnik (2000).
*The Nature of Statistical Learning Theory*, 2nd edition. Springer, New York. Transductive inference is discussed in Chapter 9. - Vladimir Vovk (2013). Transductive conformal predictors, On-line Compression Modelling Project (New Series), Working Paper 8.