The traditional Logistic regression model is transformed to multi-class kernel Logistic model applying for text-
independent speaker identification, which is nonlinear and more than just two classes. The penalty factor is added for enhancing model generalization ability. Then an iterative algorithm is proposed based on the solution of a dual problem by using ideas similar to those of the sequential minimal optimization algorithm for support vector machines. Experiments show that the algorithm is robust and fast, and the recognition rate is as good as widely used methods such as SVM while being used in text-independent speaker identification.