[Lisa_seminaires] Reminder: UdeM-McGill-MITACS machine learning seminar Fri, Sep 18th

Guillaume Desjardins guillaume.desjardins at gmail.com
Jeu 17 Sep 18:47:42 EDT 2009


Here's a friendly reminder for tomorrow's talk given by Dr.
Massih-Reza Amini and entitled "Learning from Multiple Partially
Observed Views" (see below for abstract).

This talk will be held at the University of Montreal, Pavillon
André-Aisenstadt (UdeM), room 3195 @ 11:00AM. See you there !

---------- Forwarded message ----------
From: Guillaume Desjardins <guillaume.desjardins at gmail.com>
Date: Fri, Sep 11, 2009 at 11:47 AM
Subject: [Lisa_seminaires] RESCHEDULED: UdeM-McGill-MITACS machine
learning seminar Fri, Sept
To: lisa_seminaires at iro.umontreal.ca


This is to advise you that the upcoming MITACS seminar has been
rescheduled for Friday, September 18th from 11:00-12:00, due to some
scheduling conflicts. I apologize for the short notice and hope many
of you can make it.

---------- Forwarded message ----------
From: Guillaume Desjardins <guillaume.desjardins at gmail.com>
Date: Tue, Sep 8, 2009 at 5:36 PM
Subject: UdeM-McGill-MITACS machine learning seminar (NEW DATE & TIME)
Mon, Sept 18th @11h00
To: lisa_seminaires at iro.umontreal.ca


We start off the new academic year with a talk from Dr. Massih-Reza
Amini entitled "Learning from Multiple Partially Observed Views".

Speaker: Dr. Massih-Reza Amini
             Associate Professor of  Pierre & Marie Curie University /
             Research Officer, NRC Institute for Information Technology

Location: Pavillon André-Aisenstadt (UdeM), room 3195
Time: September 18th 2009 at 11h00

Title: Learning from Multiple Partially Observed Views

Résumé: We address the problem of learning classifiers when
observations have multiple views, some of which may not be observed
for all examples. We assume the existence of view generating functions
which may complete the missing views in an approximate way. This
situation corresponds for example to learning text classifiers from
multilingual collections where documents are not available in all
languages. In that case, Machine Translation (MT) systems may be used
to translate each document in the missing languages. We derive a
generalization error bound for classifiers learned on examples with
multiple artificially created views. Our result uncovers a trade-off
between the size of the training set, the number of views, and the
quality of the view generating functions. As a consequence, we
identify situations where it is more interesting to use multiple views
for learning instead of classical single view learning. An extension
of this framework is a natural way to leverage unlabeled multi-view
data in semi-supervised learning. Experimental results on a subset of
the Reuters RCV1/RCV2 collections support our findings by showing that
additional views obtained from MT may significantly improve the
classification performance in the cases identified by our trade-off.
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