Discriminative learning from partially annotated examples
Type of document
disertační práceAuthor
Antoniuk, Kostiantyn
Supervisor
Hlaváč, Václav
Franc, Vojtěch
Field of study
Umělá inteligence a biokybernetikaStudy program
Elektrotechnika a informatikaInstitutions assigning rank
České vysoké učení technické v Praze. Fakulta elektrotechnická. Katedra kybernetikyMetadata
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A number of algorithms and its applications for automatic classi ers learning from examples
is ever growing. Most of existing algorithms require a training set of completely annotated
examples, which are often hard to obtain. In this thesis, we tackle the problem of learning
from partially annotated examples, which means that each training input comes with a set
of admissible labels only one of which is correct. We contributed to two di erent cases of
this scenario. In the rst case, we studied the problem of learning the ordinal classi ers from
examples with interval annotation of labels. We designed a convex learning algorithm for this
case and demonstrated its advantage on real data empirically. At the same time, we made
several contributions to the supervised learning of the ordinal classi ers, namely, we proposed
new parametrization of the ordinal classi er, we introduced more
exible piece wise version
of the ordinal classi er, and we proposed a generic cutting plane solver with convergence
guarantees. In the second case, we studied the problem of learning the structured output
classi ers from examples with missing annotation of a subset of labels. We have de ned
the concept of a surrogate classi cation calibrated partial loss, the minimization of which
guarantees that learning is statistical consistent under fairly general conditions on the data
generating process. We proved the existence of a convex classi cation calibrated surrogate loss
for learning from partially annotated examples. We showed which existing surrogate losses
are classi cation calibrated and which are not. Our work thus provides a missing theoretical
justi cation for so far heuristic methods which have been successfully used in practice.
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