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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/">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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