We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised...
Labeling image collections is a tedious task, especially
when multiple labels have to be chosen for each image. In
this paper we introduce a new framework that extends state
of ...
Nicolas Loeff, Ali Farhadi, Ian Endres and David A...
We propose a semi-supervised learning algorithm for discriminant analysis, which uses the geometric structure of both labeled and unlabeled samples and perform a manifold regulari...
We consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this framework, we motivate minimum entropy regulariza...
The problem of learning with positive and unlabeled examples arises frequently in retrieval applications. We transform the problem into a problem of learning with noise by labelin...