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<p class=MsoNormal><font size=2 face=Arial><span style='font-size:10.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:10.0pt;
font-family:Arial'>Guest Speaker<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:10.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:10.0pt;
font-family:Arial'>Speaker: <st1:City w:st="on"><st1:place w:st="on">Vladimir</st1:place></st1:City>
Pavlovic<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:10.0pt;
font-family:Arial'>Speaker’s home page: <a
href="http://www.cs.rutgers.edu/~vladimir">http://www.cs.rutgers.edu/~vladimir</a><o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:10.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:10.0pt;
font-family:Arial'>Date: Friday, April 20, 2007<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:10.0pt;
font-family:Arial'>Time: 2:00<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:10.0pt;
font-family:Arial'>Location: TTI-C Conference room<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:10.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:10.0pt;
font-family:Arial'>Title:<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:10.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'>Discriminative learning methods for state space models<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'>Abstract: <o:p></o:p></span></font></p>
<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'>In this talk, I will discuss discriminative learning algorithms for dynamical
systems, commonly known as the state space models<span class=GramE>. </span>Models
such as Conditional Random Fields or Maximum Entropy Markov Models outperform
the generative Hidden Markov Models in sequence tagging problems in discrete
domains<span class=GramE>. </span>However, continuous state domains introduce
different sets of constraints that make direct application of discrete state
methods challenging.<span style='mso-spacerun:yes'> </span>We suggest to two
solutions to this problem.<span style='mso-spacerun:yes'> </span>In first, we
learn generative state space models with discriminative cost functionals.<span
style='mso-spacerun:yes'> </span>For Dynamical Systems, the proposed methods
provide significantly lower prediction error than the standard maximum
likelihood estimator, often comparable to complex nonlinear models.<span
style='mso-spacerun:yes'> </span>We then discuss a set of general conditions
that result in convex learning of state space models and propose a new convex
cone algorithm that exploits these conditions. We evaluate the generalization
performance of our methods on the 3D human pose-tracking problem.<span
style='mso-spacerun:yes'> </span>The experiments indicate that the discriminative
learning can lead to improved accuracy of pose estimation with no increase in
computational cost of tracking.<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'>If you have any questions or would like to meet the <span class=GramE>speaker</span>
please contact Ponda Barnes at <a href="mailto:pondabarnes@tti-c.org">pondabarnes@tti-c.org</a>.<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'>For future TTI-C talks and event please </span></font><font size=2
face=Arial><span style='font-size:10.0pt;font-family:Arial'>go to <a
href="http://ttic.uchicago.edu/cal/monrh.php"
title="http://ttic.uchicago.edu/cal/monrh.php">http://ttic.uchicago.edu/cal/monrh.php</a><o:p></o:p></span></font></p>
<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'><o:p> </o:p></span></font></p>
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