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Subject Item
n2:RIV%2F68407700%3A21230%2F13%3A00211718%21RIV14-MSM-21230___
rdf:type
n9:Vysledek skos:Concept
rdfs:seeAlso
ftp://cmp.felk.cvut.cz/pub/cmp/articles/fojtusim/Fojtu-TR-2013-20.pdf
dcterms:description
We explore the field of supervised learning methods in the scope of domain adaptation problem. By domain adaptation we understand learning in a target domain with only a few labeled training data from the target domain, given training data or a trained classifier for a different (source) domain. Domain adaptation technique can dramatically decrease the number of training samples, which is an extremely useful feature for any machine learning problem. A unifying minimization problem is formulated, encapsulating many of the related state of the art methods. We present results of our similarity transform domain adaptation method applied to the task of vehicle detection from various viewpoints. The main goal of the thesis is to propose domain adaptation methods for sequential decision/cascaded classifiers. We explore the field of supervised learning methods in the scope of domain adaptation problem. By domain adaptation we understand learning in a target domain with only a few labeled training data from the target domain, given source training data or a trained classifier. Domain adaptation technique can dramatically decrease the number of training samples, which is an extremely useful feature for any machine learning problem. A unifying minimization problem is formulated, encapsulating many of the related state of the art methods. We present results of our similarity transform domain adaptation method applied to the task of vehicle detection from various viewpoints. The main goal of the thesis is to propose domain adaptation methods for sequential decision/cascaded classifiers. We explore the field of supervised learning methods in the scope of domain adaptation problem. By domain adaptation we understand learning in a target domain with only a few labeled training data from the target domain, given training data or a trained classifier for a different (source) domain. Domain adaptation technique can dramatically decrease the number of training samples, which is an extremely useful feature for any machine learning problem. A unifying minimization problem is formulated, encapsulating many of the related state of the art methods. We present results of our similarity transform domain adaptation method applied to the task of vehicle detection from various viewpoints. The main goal of the thesis is to propose domain adaptation methods for sequential decision/cascaded classifiers. We explore the field of supervised learning methods in the scope of domain adaptation problem. By domain adaptation we understand learning in a target domain with only a few labeled training data from the target domain, given source training data or a trained classifier. Domain adaptation technique can dramatically decrease the number of training samples, which is an extremely useful feature for any machine learning problem. A unifying minimization problem is formulated, encapsulating many of the related state of the art methods. We present results of our similarity transform domain adaptation method applied to the task of vehicle detection from various viewpoints. The main goal of the thesis is to propose domain adaptation methods for sequential decision/cascaded classifiers.
dcterms:title
Domain Adaptation for Sequential Detection -- {PhD} Thesis Proposal Domain Adaptation for Sequential Detection -- {PhD} Thesis Proposal
skos:prefLabel
Domain Adaptation for Sequential Detection -- {PhD} Thesis Proposal Domain Adaptation for Sequential Detection -- {PhD} Thesis Proposal
skos:notation
RIV/68407700:21230/13:00211718!RIV14-MSM-21230___
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n5:aktivita
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n5:aktivity
P(TA01031478), S
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n11:2014
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n18:O
n5:duvernostUdaju
n6:S
n5:entitaPredkladatele
n8:predkladatel
n5:idSjednocenehoVysledku
70347
n5:idVysledku
RIV/68407700:21230/13:00211718
n5:jazykVysledku
n7:eng
n5:klicovaSlova
domain adaptation
n5:klicoveSlovo
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n5:kontrolniKodProRIV
[376F584662C1]
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1
n5:pocetTvurcuVysledku
1
n5:projekt
n10:TA01031478
n5:rokUplatneniVysledku
n11:2013
n5:tvurceVysledku
Fojtů, Šimon
n4:organizacniJednotka
21230