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BEGIN:VEVENT
UID:/NewsandEvents/Archives/2016/newsitem/7234/8-A
 pril-2016-ACLC-Seminar-Naomi-Feldman
DTSTAMP:20160317T000000
SUMMARY:ACLC Seminar, Naomi Feldman
ATTENDEE;ROLE=Speaker:Naomi Feldman
DTSTART;TZID=Europe/Amsterdam:20160408T151500
DTEND;TZID=Europe/Amsterdam:20160408T163000
LOCATION:Rooim 4.04, PC Hoofthuis, Spuistraat 134,
  Amsterdam
DESCRIPTION:Children have impressive statistical l
 earning abilities. In phonetic category acquisitio
 n, for example, they are sensitive to the distribu
 tional properties of sounds in their input. Howeve
 r, knowing that children have statistical learning
  abilities is only a small part of understanding h
 ow they make use of their input during language ac
 quisition. This work uses Bayesian models to exami
 ne three basic assumptions that go into statistica
 l learning theories: the structure of learners' hy
 pothesis space, the way in which input data are sa
 mpled, and the features of the input that learners
  attend to. Simulations show that although a naive
  view of statistical learning may not support robu
 st phonetic category acquisition, there are severa
 l ways in which learners can potentially benefit b
 y leveraging the rich statistical structure of the
 ir input.  For more information, see http://aclc.u
 va.nl/news-and-events/events/aclc-smart-seminar/al
 l-events/ or http://ling.umd.edu/~nhf/
X-ALT-DESC;FMTTYPE=text/html:\n        <p>Children
  have impressive statistical learning abilities. I
 n phonetic category acquisition, for example, they
  are sensitive to the distributional properties of
  sounds in their input. However, knowing that chil
 dren have statistical learning abilities is only a
  small part of understanding how they make use of 
 their input during language acquisition. This work
  uses Bayesian models to examine three basic assum
 ptions that go into statistical learning theories:
  the structure of learners' hypothesis space, the 
 way in which input data are sampled, and the featu
 res of the input that learners attend to. Simulati
 ons show that although a naive view of statistical
  learning may not support robust phonetic category
  acquisition, there are several ways in which lear
 ners can potentially benefit by leveraging the ric
 h statistical structure of their input.</p>\n    \
 n        <p>For more information, see <a target="_
 blank" href="http://aclc.uva.nl/news-and-events/ev
 ents/aclc-smart-seminar/all-events/all-events/cont
 ent-2/folder/2016/04/8-aclc-seminar-feldman.html">
 http://aclc.uva.nl/news-and-events/events/aclc-sma
 rt-seminar/all-events/</a> or <a target="_blank" h
 ref="http://ling.umd.edu/~nhf/">http://ling.umd.ed
 u/~nhf/</a></p>\n    
URL:/NewsandEvents/Archives/2016/newsitem/7234/8-A
 pril-2016-ACLC-Seminar-Naomi-Feldman
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