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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F13%3A00212095%21RIV14-MSM-21230___
rdf:type
n11:Vysledek skos:Concept
rdfs:seeAlso
ftp://cmp.felk.cvut.cz/pub/cmp/articles/antoniuk/Antoniuk-TR-2013-22.pdf
dcterms:description
In this report, we outline the open issues and topics for the work towards the PhD thesis. The topics we plan to investigate are related to the methods for discriminative learning from the partially annotated examples. We put emphasis on the structured output classification where such learning methods are desperately needed. However, we are fully aware of a very high risk connected with this topic because the most straightforward ideas have been already exploited by others without a big success. In order to have a less risky contingency plan we also intend to investigate some new ideas for %22flat%22 classification with the hope that they can be further generalized to the SOL. Report contains overview of current state-of-the-art methods and our contribution to the field. In this report, we outline the open issues and topics for the work towards the PhD thesis. The topics we plan to investigate are related to the methods for discriminative learning from the partially annotated examples. We put emphasis on the structured output classification where such learning methods are desperately needed. However, we are fully aware of a very high risk connected with this topic because the most straightforward ideas have been already exploited by others without a big success. In order to have a less risky contingency plan we also intend to investigate some new ideas for %22flat%22 classification with the hope that they can be further generalized to the SOL. Report contains overview of current state-of-the-art methods and our contribution to the field.
dcterms:title
Discriminative Methods for Semi-supervised Learning Discriminative Methods for Semi-supervised Learning
skos:prefLabel
Discriminative Methods for Semi-supervised Learning Discriminative Methods for Semi-supervised Learning
skos:notation
RIV/68407700:21230/13:00212095!RIV14-MSM-21230___
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n12:orjk%3A21230
n3:aktivita
n10:S n10:P
n3:aktivity
P(1M0567), P(TE01020197), S
n3:dodaniDat
n16:2014
n3:domaciTvurceVysledku
Antoniuk, Kostiantyn
n3:druhVysledku
n7:O
n3:duvernostUdaju
n13:S
n3:entitaPredkladatele
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n3:idSjednocenehoVysledku
69854
n3:idVysledku
RIV/68407700:21230/13:00212095
n3:jazykVysledku
n18:eng
n3:klicovaSlova
Learning from partitial annotations; Semi-supervised learning; SVM; S3VM; BMRM; ACCPM; ordinal regression; age recognition; Markov Random Fields
n3:klicoveSlovo
n4:Semi-supervised%20learning n4:ACCPM n4:ordinal%20regression n4:SVM n4:age%20recognition n4:BMRM n4:Markov%20Random%20Fields n4:S3VM n4:Learning%20from%20partitial%20annotations
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[7FA7209B1EFA]
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1
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1
n3:projekt
n8:1M0567 n8:TE01020197
n3:rokUplatneniVysledku
n16:2013
n3:tvurceVysledku
Antoniuk, Kostiantyn
n17:organizacniJednotka
21230