Online　Multiple　Kernel　Classification　(OMKC)　algorithm　has　been　a　popular　method　for　exploring　effective　online　combination　of　multiple　kernel　classifiers.　The　framework　for　OMKC　is　commonly　obtained　by　learning　multiple　kernel　classifiers　and　simultaneously　their　linear　combination.　However,　the　traditional　perceptron　algorithm　which　OMKC　algorithm　bases　on　does　not　achieve　a　much　smaller　mistake　rate.　In　this　paper,　we　put　forward　a　novel　algorithm　based　on　OMKC　using　an　improved　perceptron　algorithm.　Our　perceptron　algorithm　is　applied　with　the　best　kernel.　The　algorithm　produces　an　online　validation　procedure　to　search　for　the　best　kernel　among　the　pool　of　kernels　using　the　first　10%　training　examples.　By　using　histograms　analysis,　our　proposed　algorithm　can　achieve　smaller　mistake　rate　and　less　time　consuming.　Extensive　experimental　results　on　twelve　data　sets　demonstrate　the　effectiveness　and　efficiency　of　our　algorithm.　©　2014　IEEE.