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Standard candles / Data mining / Support vector machine / Supernova / Cross-validation / Cluster analysis / Receiver operating characteristic / Supervised learning / Cosmic distance ladder / Statistics / Machine learning / Statistical classification


Supernova Recognition using Support Vector Machines Raquel A. Romano Cecilia R. Aragon Chris Ding
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Document Date: 2009-06-12 16:13:25

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City

Oakland / New York / Berkeley / /

Company

Multiple Classifier Systems / Ding Computational Research Division Lawrence Berkeley National Laboratory / Neural Networks / Other Telescope Technologies / ACM Press / /

Country

United States / /

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Facility

National Energy Research Scientific Computing Center / University of Hawaii / Aragon Chris Ding Computational Research Division Lawrence Berkeley National Laboratory / Nearby Supernova Factory / Parallel Distributed Systems Facility / /

IndustryTerm

image processing improvements / grid search / parameter grid search / Statistical learning algorithms / Wireless Research / inner product / dark energy / supernova search / search software / imaging / /

MarketIndex

set 4000 / set 5000 / /

NaturalFeature

Mt. Palomar / Mauna Kea / /

Organization

National Energy Research Scientific Computing Center / University of Hawaii / /

Person

Survey / Raquel A. Romano Cecilia / /

ProvinceOrState

Hawaii / California / New York / /

PublishedMedium

Astronomical Journal / Astronomy and Astrophysics / Lecture Notes in Computer Science / /

Technology

Bioinformatics / Statistical learning algorithms / Data Mining / Supernova Recognition The SVM algorithm / machine learning / Automated Clustering Algorithms / image processing / /

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