Computer Science – Learning
Scientific paper
Jan 2012
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012aas...21914512d&link_type=abstract
American Astronomical Society, AAS Meeting #219, #145.12
Computer Science
Learning
Scientific paper
The exponential growth of data volumes and complexity in astronomy, as in almost every other field of science, presents both great opportunities and great challenges for an effective knowledge discovery. We describe DAta Mining and Exploration (DAME), a general purpose, Web-based, distributed infrastructure for an effective data mining in massive and complex data sets. DAME includes machine-learning tools such as a variety of Artificial Neural Networks, Support Vector Machines, Self-Organizing Maps, Bayesian Networks, etc., for tasks such as an automated classification or regression fitting in multi-dimensional parameter spaces, etc. DAME also provides workspaces and grid access mechanisms, as well as an extensive documentation and user guides. We illustrate DAME applications on several scientific examples. DAME represents a new generation of astroinformatics tools that will become increasingly important for the data-rich astronomy in the 21st century.
Brescia Massimo
Cavuoti Stefano
d'Abrusco Raffaele
Djorgovski Stanislav G.
Donalek Ciro
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