TRIANGULAR HERMITE KERNEL EXTREME LEARNING MACHINE

摘要

The triangular Hermite kernel extreme learning machine methodology is presented based on Hermite polynomial. It introduces the triangular Hermite function which has been proved as a valid kernel function into extreme learning machine as kernel function. The most significant advantages of proposed kernel are that it has only one parameter chosen from a small set of natural numbers, thus the parameter optimization is facilitated greatly, and more structure information of sample data is retained. Experiments were performed on bi-spiral benchmark data set as well as a number of regression datasets from the UCI benchmark repository. Similar or better robustness and generalization performance of the proposed method in comparison to other extreme learning machine with different kernels and SVM (Support Vector Machine) methods demonstrates its effectiveness and usefulness.