In　this　paper,　we　propose　a　least　squares　regularized　regression　algorithm　with　l(1)-regularizer　in　a　sum　space　of　some　base　hypothesis　spaces.　This　sum　space　contains　more　functions　than　single　base　hypothesis　space　and　therefore　has　stronger　approximation　capability.　We　establish　an　excess　error　bound　for　this　algorithm　under　some　assumptions　on　the　kernels,　the　input　space,　the　marginal　distribution　and　the　regression　function.　For　error　analysis,　the　excess　error　is　decomposed　into　the　sample　error,　hypothesis　error　and　regularization　error,　which　are　estimated　respectively.　From　the　excess　error　bound,　convergency　and　a　learning　rate　can　be　derived　by　choosing　a　suitable　value　of　the　regularization　parameter.　The　utility　of　this　method　is　illustrated　with　two　simulated　data　sets　and　one　real　life　database.　(C)　2013　Elsevier　B.V.　All　rights　reserved.