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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00211718" target="_blank" >RIV/68407700:21230/13:00211718 - isvavai.cz</a>
Result on the web
<a href="http://cmp.felk.cvut.cz/pub/cmp/articles/fojtusim/Fojtu-TR-2013-20.pdf" target="_blank" >http://cmp.felk.cvut.cz/pub/cmp/articles/fojtusim/Fojtu-TR-2013-20.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Domain Adaptation for Sequential Detection -- {PhD} Thesis Proposal
Original language 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 train
Czech name
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Czech description
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Project
<a href="/en/project/TA01031478" target="_blank" >TA01031478: Automatic monitoring of transport flow including noise</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach

Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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