@TechReport{Fojtu-TR-2013-20,
  IS = { zkontrolovano 15 Jan 2014 },
  UPDATE  = { 2013-09-19 },
author =      {Fojt{\accent23u}, {{\v S}}imon},
supervisor =  {Zimmermann, Karel and Hlav{\' a}{\v c}, V{\'a}clav},
title =       {Domain Adaptation for Sequential Detection -- {PhD} Thesis Proposal},
institution = {Center for Machine Perception, K13133 FEE
               Czech Technical University},
address =     {Prague, Czech Republic},
year =        {2013},
month =       {August},
type =        {Research Report},
number =      {CTU--CMP--2013--20},
issn =        {1213-2365},
pages =       {33},
figures =     {6},
authorship =  {100},
psurl       = {[Fojtu-TR-2013-20.pdf]},
project =     {TACR TA01031478 AUTMODO, SGS12/187/OHK3/3T/13},
annote =      {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.},
keywords =    {domain adaptation},
}