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Department of Machine Learning
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Transduction and Semi-Supervised Learning 
Transduction is a new learning principle which
combines induction and deduction in a single step, and is related
to the field of semi-supervised learning where one uses unlabeled data
during learning.
By eliminating the need to construct an accurate model,
transduction provides opportunities to achieve greater accuracy,
as has been demonstrated in text analysis and bio-informatics applications.
Structured Output Learning 
We study learning problems where the predictions are structured objects rather than vectors,
for example parse trees or strings.
Universum-based Learning 
We study a new framework introduced by (Vapnik 1998)
that is an alternative capacity concept to the large margin approach of SVMs.
In this setting, one is given a set of
labeled examples, and a collection of "non-examples" that do not belong
to either class of interest. This collection, called the Universum,
allows one to encode prior knowledge by representing meaningful
concepts in the same domain as the problem at hand.
SVM+ 
SVM+ is a new approach to use hidden information within the learning framework.
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