首页|Reports Summarize Machine Learning Study Results from Planetary Science Institute (A Machine Learning Classification of Meteorite Spectra Applied To Understanding Asteroids)
Reports Summarize Machine Learning Study Results from Planetary Science Institute (A Machine Learning Classification of Meteorite Spectra Applied To Understanding Asteroids)
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NETL
NSTL
Elsevier
Research findings on Machine Learning are discussed in a new report. According to news reporting from Tucson, Arizona, by NewsRx journalists, research stated, “Understanding the distribution of matter within our Solar System requires a robust methodology for evaluating the composition of small objects in the asteroid belt. Existing asteroid taxonomies have variously been based on spectral features relating to mineralogy and on classification of asteroid spectra alone.” Financial supporters for this research include Massachusetts Space Grant Consortium, via NASA, National Science Foundation (NSF), National Aeronautics & Space Administration (NASA). The news correspondents obtained a quote from the research from Planetary Science Institute, “This project tests a fundamentally different approach, using machine learning algorithms to classify asteroids based on spectroscopic characteristics of existing meteorite classes. After evaluating four classification techniques built on labeled meteorite spectral data, logistic regression (LR) was determined to provide the most accurate results that distinguish eight robust groups of meteorite classes to which asteroid spectra can then be matched. The groups are rooted in mineralogical composition and directly relate meteorites to potential host bodies.”
TucsonArizonaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningPlanetary Science Institute
NETL
NSTL
Elsevier
