Based　on　the　wavelet　decomposition　and　conditions　of　the　support　vector　kernel　function,　Morlet　wavelet　kernel　function　for　support　vector　machine　(SVM)　is　proposed,　which　is　a　kind　of　approximately　orthonormal　function.　This　kernel　function　can　simulate　almost　any　curve　in　quadratic　continuous　integral　space,　thus　it　enhances　the　generalization　ability　of　the　SVM.　According　to　the　wavelet　kernel　function　and　the　regularization　theory,　Least　squares　support　vector　machine　on　Morlet　wavelet　kernel　function　(LS-MWSVM)　is　proposed　to　simplify　the　process　of　MWSVM.　The　LS-MWSVM　is　then　applied　to　the　regression　analysis　or　classifying.　Experiment　results　show　that　the　regression＇s　precision　is　improved　by　LS-MWSVM,　compared　with　LS-SVM　whose　kernel　function　is　Gauss　function　under　the　same　conditions.　©　2005　IEEE.