"RIV/68407700:21230/13:00211718!RIV14-MSM-21230___" . "21230" . . "domain adaptation"@en . . . . . "[376F584662C1]" . "RIV/68407700:21230/13:00211718" . . "70347" . . . . "Domain Adaptation for Sequential Detection -- {PhD} Thesis Proposal"@en . "P(TA01031478), S" . . "Domain Adaptation for Sequential Detection -- {PhD} Thesis Proposal" . "ftp://cmp.felk.cvut.cz/pub/cmp/articles/fojtusim/Fojtu-TR-2013-20.pdf" . . "1"^^ . . "Domain Adaptation for Sequential Detection -- {PhD} Thesis Proposal"@en . . . "Domain Adaptation for Sequential Detection -- {PhD} Thesis Proposal" . "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 . "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." . "1"^^ . . "Fojt\u016F, \u0160imon" .