[Solomonov Seminar] 287. Solomonov seminar

[Marko Grobelnik]

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 http://videolectures.net/solomon/

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Title: Learning in a dynamic and ever changing world

Lecturer: Geoff Webb, Monash University, Australia

Abstract:

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.

Bio:

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).

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