5 search results - page 1 / 1 » Synthesis of maximum margin and multiview learning using unl... |
ESANN
2006
13 years 6 months ago
2006
In this presentation we show the semi-supervised learning with two input sources can be transformed into a maximum margin problem to be similar to a binary SVM. Our formulation exp...
JMLR
2010
13 years 8 days ago
2010
In this paper, we propose a general framework for sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent targe...
ECCV
2010
Springer
13 years 10 months ago
2010
Springer
Many learning tasks for computer vision problems can be described by multiple views or multiple features. These views can be exploited in order to learn from unlabeled data, a.k.a....
NIPS
2004
13 years 6 months ago
2004
We propose a new method for clustering based on finding maximum margin hyperplanes through data. By reformulating the problem in terms of the implied equivalence relation matrix, ...
JMLR
2010
13 years 8 days ago
2010
In this paper, we present an overview of generalized expectation criteria (GE), a simple, robust, scalable method for semi-supervised training using weakly-labeled data. GE fits m...