Large Scale Learning - Challenge

author: Sören Sonnenburg, Machine Learning and Intelligent Data Analysis Group, TU Berlin
author: Vojtech Franc, Fraunhofer Institute for Intelligent Analysis and Information Systems
published: Sept. 1, 2008,   recorded: July 2008,   views: 3696
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Description

With the exceptional increase in computing power, storage capacity and network bandwidth of the past decades, ever growing datasets are collected in fields such as bioinformatics (Splice Sites, Gene Boundaries, etc), IT-security (Network traffic) or Text-Classification (Spam vs. Non-Spam), to name but a few. While the data size growth leaves computational methods as the only viable way of dealing with data, it poses new challenges to ML methods. This workshop is concerned with the scalability and efficiency of existing ML approaches with respect to computational, memory or communication resources, e.g. resulting from a high algorithmic complexity, from the size or dimensionality of the data set, and from the trade-off between distributed resolution and communication costs.

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Download slides icon Download slides: icml08_sonnenburg_lsl_01.pdf (1.7 MB)


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