Learning and modelling big data
Document Type
Conference Proceeding
Date of Original Version
1-1-2014
Abstract
Caused by powerful sensors, advanced digitalisation techniques, and dramatically increased storage capabilities, big data in the sense of large or streaming data sets, very high dimensionality, or complex data formats constitute one of the major challenges faced by machine learning today. In this realm, a couple of typical assumptions of machine learning can no longer be met, such as e.g. the possibility to deal with all data in batch mode or data being identically distributed; this causes the need for novel algorithmic developments and paradigm shifts, or for the adaptation of existing ones to cope with such situations. The goal of this tutorial is to give an overview about recent machine learning approaches for big data, with a focus on principled algorithmic ideas in the field.
Publication Title, e.g., Journal
22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Proceedings
Citation/Publisher Attribution
Hammer, Barbara, Haibo He, and Thomas Martinetz. "Learning and modelling big data." 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Proceedings (2014): 343-352. https://digitalcommons.uri.edu/ele_facpubs/535