Applying the Support Vector Machine Method to Matching IRAS and SDSS Catalogues | Zendy

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Applying the Support Vector Machine Method to Matching IRAS and SDSS Catalogues

Author(s) -

Chen Cao

Publication year - 2007

Publication title -

data science journal

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.358

H-Index - 21

ISSN - 1683-1470

DOI - 10.2481/dsj.6.s756

Subject(s) - computer science , scope (computer science) , usability , transparency (behavior) , open data , metadata , implementation , data science , open science , reuse , electronic publishing , software , world wide web , information retrieval , the internet , software engineering , engineering , physics , computer security , human–computer interaction , astronomy , programming language , waste management

This paper presents results of applying a machine learning technique, the Support Vector Machine (SVM), to the astronomical problem of matching the Infra-Red Astronomical Satellite (IRAS) and Sloan Digital Sky Survey (SDSS) object catalogues. In this study, the IRAS catalogue has much larger positional uncertainties than those of the SDSS. A model was constructed by applying the supervised learning algorithm (SVM) to a set of training data. Validation of the model shows a good identification performance (∼ 90% correct), better than that derived from classical cross-matching algorithms, such as the likelihood-ratio method used in previous studies

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