Novelty　and　outlier　detection　are　both　used　for　anomaly　detection.　This　paper　works　through　the　method　of　One-Class　support　vector　machine　(SVM)　which　could　estimate　the　contour　of　initial　observations　and　can　be　applied　in　the　problems　of　anomaly　detection.　In　this　paper,　the　experiments　on　both　artificial　and　real-world　data　sets　are　performed　to　demonstrate　the　importance　of　kernel　and　kernel　parameter　choice　and　the　corresponding　sensitivity　of　the　algorithm.　Due　to　the　desire　for　general　regulations　of　kernel　parameter　selection,　some　general　methods　of　selecting　the　kernel　bandwidth　parameter　of　RBF　kernel　are　therefore　investigated,　including　median　heuristic　method　and　Bayesian　kernel　learning　method.　Then,　the　experiments　based　on　these　methods　are　conducted　to　observe　their　effect.　Consequently,　some　new　discoveries　together　with　current　issues　on　these　methods,　as　well　as　some　applicable　situations　of　these　methods,　are　found　in　this　paper.　Furthermore,　this　paper　also　confirms　the　effectiveness　of　RBF　kernel　and　that　the　initial　value　of　its　bandwidth　parameter　can　be　set　by　those　two　methods.　©　2020　IEEE.