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SUMMARY:The Magic of Machine Learning and Classifier ensembles - Ludmila K
 uncheva\,Bangor University
DTSTART:20190130T161500Z
DTEND:20190130T170000Z
UID:TALK117139@talks.cam.ac.uk
CONTACT:Rafal Mantiuk
DESCRIPTION:Deep learning neural networks (Convolutional Neural Networks (
 CNN)) have dominated the landscape of machine learning and pattern recogni
 tion for over a decade now\, at least in vision\, speech and language reco
 gnition. While numerous bespoke CNN models compete for the top places in p
 opular world-wide contests\, these classifiers are far from ideal. To rais
 e their accuracy further\, ideas and algorithms from the classical pattern
  recognition may prove useful. In this line we consider combining classifi
 ers into an ensemble. The aim is to offer a more accurate and robust class
 ification decision compared to that of a single classifier. For a successf
 ul ensemble\, the individual classifiers must be as diverse and as accurat
 e as possible. While diversity has been a focus for a long time now\, the 
 combination rule of the individual votes has often been marginalised. This
  talk will introduce classifier ensembles along with some combination rule
 s. Using a MATLAB demo we will demonstrate the importance of the diversity
  of the individual classifiers in the ensemble and the merit of choosing a
  suitable combination rule.
LOCATION:Lecture Theatre 2\, Computer Laboratory
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