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SISAP 2017: On Competitiveness of Nearest-Neighbor-Based Music Classification: A Methodological Critique Authors: Haukur Pálmason, Björn Þór Jónsson, Laurent Amsaleg, Markus Schedl, and Peter Knees Björn Þór Jónsson is presenting a paper at the SISAP conference ( in München. The traditional role of nearest-neighbor classification in music classification research is that of a straw man opponent for the learning approach of the hour. Recent work in high-dimensional indexing has shown that approximate nearest-neighbor algorithms are extremely scalable, yielding results of reasonable quality from billions of high- dimensional features.

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Big data from space to be used in ITU classrooms A new collaboration between the IT University of Copenhagen and the Danish Agency for Science and Higher Education gives students and researchers the chance to work with data collected by the EU’s earth observation program, Copernicus. Each Copernicus satellite produces 1.5 petabytes of raw data annually, holding an enormous potential for increasing the understanding of our planet. Since dissecting the data takes skills within software development and data analysis, ITU and the Danish Agency for Science and Higher Education have formed a new collaboration.

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