Learning and modelling big data
Year: 2014
Type: article
Abstract: . Caused by powerful sensors, advanced digitalisation tech-niques, and dramatically increased storage capabilities, big data in the sense of large or streaming data sets, very high dimensionality, or com-plex 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. 1
Authors: Barbara Hammer, Haibo He, Thomas Martinetz
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2558942002