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<font face="Times New Roman, Times, serif">Popravek - 287. Solomonov
seminar bo potekal v <b>Oranžni predavalnici</b> (drugo
nadstropje glavne stavbe). Predavalnica je v drugem nadstropju
glavne stavbe IJS na Jamovi 39.<br>
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<pre class="moz-signature" cols="72">--
Mateja Škraba
Jozef Stefan Institute
Artificial Intelligence Laboratory
Jamova cesta 39, 1000 Ljubljana
Slovenia
Tel: +386 40 214 982
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<div class="moz-cite-prefix">On 27.3.2019 0:37, Marko Grobelnik
wrote:<br>
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cite="mid:a2f8b231-d02a-1948-19a9-83b37f6c7441@ijs.si">
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V cetrtek, 28. marca 2019 bo ob 11h v Oranzni predavalnici (drugo
nadstropje glavne stavbe) 287. Solomonov seminar. Velika
predavalnica je v prvem nadstropju glavne stavbe IJS na Jamovi 39.
Posnetki preteklih seminarjev so na <a
class="moz-txt-link-freetext"
href="http://videolectures.net/solomon/" moz-do-not-send="true">http://videolectures.net/solomon/</a>
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<tt><span
style="color:windowtext" lang="EN-AU"><br>
Title: Learning in a dynamic and ever changing world</span></tt>
<p class="MsoNormal"><tt><span style="color:windowtext"
lang="EN-AU">Lecturer: Geoff Webb, Monash University,
Australia<br>
</span></tt></p>
<p class="MsoNormal"><tt><span style="color:windowtext"
lang="EN-AU">Abstract:</span></tt></p>
<p class="MsoNormal"><tt><span style="color:windowtext"
lang="EN-AU">The world is dynamic – in a constant state of
flux – but most learned models are static. Models learned
from historical data are likely to decline in accuracy over
time. I will present our recent work on how to address this
serious issue that confronts many real-world applications of
machine learning. Methodology: we are developing objective
quantitative measures of drift and effective techniques for
assessing them from sample data. Theory: we posit a strong
relationship between drift rate, optimal forgetting rate and
optimal bias/variance profile, with the profound implication
that the fundamental nature of a learning algorithm should
ideally change as drift rate changes. Techniques: we have
developed the Extremely Fast Decision Tree, a statistically
more efficient variant of the incremental learning
workhorse, the Very Fast Decision Tree.</span></tt></p>
<p class="MsoNormal"><tt><span style="color:windowtext"
lang="EN-AU">Bio:</span></tt></p>
<p class="MsoNormal"><tt><span style="color:windowtext"
lang="EN-AU">Professor Geoff Webb is Director of the Monash
University Center for Data Science. He was editor in chief
of the leading data mining journal, Data Mining and
Knowledge Discovery, from 2005 to 2014. He has been Program
Committee Chair of both the leading data mining conferences,
ACM SIGKDD and IEEE ICDM, as well as General Chair of ICDM.
He is a Technical Advisor to machine learning as a service
startup BigML Inc and to recommender systems startup
FROOMLE. He developed many of the key mechanisms of
support-confidence association discovery in the 1980s. His
OPUS search algorithm remains the state-of-the-art in rule
search. He pioneered multiple research areas as diverse as
black-box user modelling, interactive data analytics and
statistically-sound pattern discovery. He has developed
many useful machine learning algorithms that are widely
deployed. His many awards include IEEE Fellow and the
inaugural Eureka Prize for Excellence in Data Science
(2017).</span></tt></p>
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