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pro vyhledávání: '"Kauschke, Sebastian"'
Autor:
Kauschke, Sebastian
Machine learning models are subject to changing circumstances, and will degrade over time. Nowadays, data are collected in vast amounts: Personal data is retrieved by our phones, by our internet browser, via our shopping behavior, and especially thro
In this report we investigate fundamental requirements for the application of classifier patching on neural networks. Neural network patching is an approach for adapting neural network models to handle concept drift in nonstationary environments. Ins
Externí odkaz:
http://arxiv.org/abs/1812.03468
Publikováno v:
Proceedings ESANN 2019
With today's abundant streams of data, the only constant we can rely on is change. For stream classification algorithms, it is necessary to adapt to concept drift. This can be achieved by monitoring the model error, and triggering counter measures as
Externí odkaz:
http://arxiv.org/abs/1811.10900
Publikováno v:
Discovery Science (9783319463063); 2016, p151-166, 16p
