Scalable Learning of Graphical Models
author: François Petitjean, Faculty of Information Technology, Monash University
published: Sept. 9, 2016, recorded: August 2016, views: 1938
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From understanding the structure of data, to classification and topic modeling, graphical models are core tools in machine learning and data mining. They combine probability and graph theories to form a compact representation of probability distributions. In the last decade, as data stores became larger and higher-dimensional, traditional algorithms for learning graphical models from data, with their lack of scalability, became less and less usable, thus directly decreasing the potential benefits of this core technology. To scale graphical modeling techniques to the size and dimensionality of most modern data stores, data science researchers and practitioners now have to meld the most recent advances in numerous specialized fields including graph theory, statistics, pattern mining and graphical modeling.
This tutorial covers the core building blocks that are necessary to build and use scalable graphical modeling technologies on large and high-dimensional data.
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