Recognition of gene promoter DNA sequence is difficult with the complex structure and the huge amount of data.In this paper

the positional densities of oligonucleotides are modeled by Gaussian mixture model.It can identify less frequent but important motifs

since the positional density is independent of the actual occurrence frequency of the oligonucleotide.These motifs generally correspond to the consensus sequences of transcription factor binding site.GMM is used as eukaryotic promoter LS-SVM kernel

which simplifies the LS-SVM as LS model.The algorithm is simplified and the computational complexity is decreased.The simulation results show the accuracy is improved compared with Bayesian classifier

and is same to LS-SVM with RBF kernel

moreover the model building time is shorter.