Machine Learning for Searching the Dark Energy Survey for Trans-Neptunian Objects.
Ben HenghesOfer LahavDavid W. GerdesE. LinRobert MorganTimothy M. C. AbbottMichel AguenaSahar AllamJames AnnisSantiago ÁvilaEmmanuel BertinD. BrooksDavid L. BurkeAurelio Carnero RosellMatias Carrasco KindJorge CarreteroChristopher J. ConseliceM. CostanziLuiz Nicolaci da CostaJ. DeVicenteShantanu DesaiH. Thomas DiehlPeter DoelS. EverettI. FerreroJoshua A. FriemanJuan García-BellidoEnrique GaztañagaDaniel GruenRobert A. GruendlJulia GschwendG. GutierrezWilliam G. HartleySamuel R. HintonKlaus HonscheidBen HoyleDavid J. JamesK. KuehnNikolay KuropatkinJennifer L. MarshallPeter MelchiorFelipe MenanteauRamon MiquelRicardo OgandoAntonella PalmeseFrancisco Paz-ChinchónAndreas Alejandro PlazasAnita K. RömerCarles SánchezEusebio SánchezVictor E. ScarpineMichael S. SchubnellSantiago SerranoM. SmithMarcelle Soares-SantosEric SuchytaGregory G. TarléC. ToR. D. WilkinsonPublished in: CoRR (2020)
Keyphrases
- machine learning
- pattern recognition
- learning algorithm
- energy consumption
- neural network
- decision trees
- text mining
- complex scenes
- object model
- inductive learning
- machine learning methods
- model selection
- data collection
- d objects
- knowledge representation
- reinforcement learning
- computer vision
- information extraction
- supervised learning
- active learning
- viewpoint
- moving objects
- knowledge base
- search strategies
- spatial relations
- feature selection
- artificial intelligence