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Expand Up @@ -106,7 +106,7 @@ Motivation
===============

Current semi-supervised learning approaches require strong assumptions, and perform badly if those
assumptions are violated (e.g. low density assumption, clustering assumption). In some cases, they can perform worse than a supervised classifier trained only on the labeled exampels. Furthermore, the vast majority require O(N^2) memory.
assumptions are violated (e.g. low density assumption, clustering assumption). In some cases, they can perform worse than a supervised classifier trained only on the labeled examples. Furthermore, the vast majority require O(N^2) memory.

[(Loog, 2015)](http://arxiv.org/abs/1503.00269) has suggested an elegant framework (called Contrastive Pessimistic Likelihood Estimation / CPLE) which
**only uses assumptions intrinsic to the chosen classifier**, and thus allows choosing likelihood-based classifiers which fit the domain / data
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