Journal article

Privileged information for data clustering

Jan Feyereisl, Uwe Aickelin

INFORMATION SCIENCES | ELSEVIER SCIENCE INC | Published : 2012

Abstract

Many machine learning algorithms assume that all input samples are independently and identically distributed from some common distribution on either the input space X, in the case of unsupervised learning, or the input and output space X × Y in the case of supervised and semi-supervised learning. In the last number of years the relaxation of this assumption has been explored and the importance of incorporation of additional information within machine learning algorithms became more apparent. Traditionally such fusion of information was the domain of semi-supervised learning. More recently the inclusion of knowledge from separate hypothetical spaces has been proposed by Vapnik as part of the ..

View full abstract

University of Melbourne Researchers