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Context. In modern astronomy, machine learning has proved to be efficient andeffective to mine the big data from the newesttelescopes. Spectral surveysenable us to characterize millions of objects, while long exposure timeobservations and wide surveysconstrain their strides from millions to billions.Aims.In this study, we construct a supervised machine learning algorithm, toclassify the objects in the Javalambre Photometric LocalUniverse Survey firstdata release (J-PLUS DR1). Methods.The sample set is featured with 12-wavebandphotometry, and magnitudes are labeled with spectrum-based catalogs, in-cludingSloan Digital Sky Survey spectroscopic data, Large Sky Area Multi-Object FiberSpectroscopic Telescope, and VERONCAT- Veron Catalog of Quasars & AGN. Theperformance of the classifier is presented with applications of blind testvalidations basedon RAdial Velocity Extension, Kepler Input Catalog, 2 MASSRedshift Survey, and the UV-bright Quasar Survey. A new algorithmis applied toconstrain the extrapolation that could decrease accuracies for many machinelearning classifiers. Results.The accuracies of the classifier are 96.5% inblind test and 97.0% in training cross validation. The F1-scores for eachclassare presented to show the precision of the classifier. We also discussdifferent methods to constrain the po

J-PLUS: Support Vector Machine Applied to STAR-GALAXY-QSOClassification