A　fast　coreset　minimum　enclosing　ball　kernel　algorithm　was　proposed.　First,　it　transfers　the　kernel　methods　to　a　center-constrained　minimum　enclosing　ball　problem,　and　subsequently　it　trains　the　kernel　methods　using　the　proposed　MEB　algorithm,　and　the　primal　variables　of　the　kernel　methods　are　recovered　via　KKT　conditions.　Then,　detailed　theoretical　analysis　and　rigid　proofs　of　our　new　algorithm　are　given.　After　that,　experiments　are　investigated　via　using　several　typical　classification　datasets　from　UCI　machine　learning　benchmark　datasets.　Moreover,　performances　compared　with　standard　support　vector　machines　are　seriously　considered.　It　is　concluded　that　our　proposed　algorithm　owns　comparable　even　superior　performances　yet　with　rather　fast　converging　speed　in　the　experiments　studied　in　this　paper.　Finally,　comments　about　the　existing　problems　and　future　development　directions　are　discussed.