[Solomonov Seminar] 226. Solomonov seminar

[Marko Grobelnik]

V torek 22. februarja bo ob 13h ob 13:00h v Oranzni predavalnici IJS
(drugo nadstropje glavne zgradbe IJS), 226. Solomonov seminar.
Posnetki preteklih seminarjev so na http://videolectures.net/solomon/

Na seminarju bo Michelangelo Ceci iz Univerze v Bariju
predstavil raziskave na podrocju aktualnega podrocja
pol-nadzorovanega ucenja (semi-supervised learning).


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Michelangelo Ceci
    Dipartimento di Informatica, Universita  degli Studi di Bari
    http://www.di.uniba.it/~ceci/

Semisupervised learning

During recent years, there has been a growing interest in learning
algorithms capable of utilizing both labeled and unlabeled data for
prediction tasks. The reason for this attention is the cost of assigning
labels which can be very high for large datasets. Two main settings
have been proposed in the literature to exploit information contained
in both labeled and unlabeled data: the semi-supervised setting and
the transductive setting. The former is a type of inductive learning,
since the learned function is used to make predictions on any possible
observation. The latter asks for less, since it is only interested
in making predictions for a set of unlabeled data known at the learning 
time.

By focusing on the transductive setting, we discuss the underlying
smoothness assumption and its validity for several data types
characterized by (positive) autocorrelation, such as spatial and
networked data. In particular, we report of the application of
transductive learning approaches to these data types and results
obtained in domains characterized by scarcity of labelled data.
Finally, we discuss the transductive setting in the more general
perspective of relational data mining.