About: Domain Adaptation for Sequential Detection -- {PhD} Thesis Proposal

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

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rdfs:seeAlso	ftp://cmp.felk.cvut.cz/pub/cmp/articles/fojtusim/Fojtu-TR-2013-20.pdf
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. (en)
Title	Domain Adaptation for Sequential Detection -- {PhD} Thesis Proposal Domain Adaptation for Sequential Detection -- {PhD} Thesis Proposal (en)
skos:prefLabel	Domain Adaptation for Sequential Detection -- {PhD} Thesis Proposal Domain Adaptation for Sequential Detection -- {PhD} Thesis Proposal (en)
skos:notation	RIV/68407700:21230/13:00211718!RIV14-MSM-21230___
http://linked.open...avai/predkladatel	Fakulta elektrotechnická
http://linked.open...avai/riv/aktivita	P S
http://linked.open...avai/riv/aktivity	P(TA01031478), S
http://linked.open...vai/riv/dodaniDat	2014
http://linked.open...aciTvurceVysledku	Fojtů, Šimon
http://linked.open.../riv/druhVysledku	O - Ostatní výsledky nezařaditelné do žádného z výše uvedených druhů výsledku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta elektrotechnická
http://linked.open...dnocenehoVysledku	70347
http://linked.open...ai/riv/idVysledku	RIV/68407700:21230/13:00211718
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	domain adaptation (en)
http://linked.open.../riv/klicoveSlovo	domain adaptation
http://linked.open...ontrolniKodProRIV	[376F584662C1]
http://linked.open...in/vavai/riv/obor	JD
http://linked.open...ichTvurcuVysledku	1 (xsd:int)
http://linked.open...cetTvurcuVysledku	1 (xsd:int)
http://linked.open...vavai/riv/projekt	Automatic monitoring of transport flow including noise
http://linked.open...UplatneniVysledku	2013
http://linked.open...iv/tvurceVysledku	Fojtů, Šimon
http://localhost/t...ganizacniJednotka	21230

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