  Kernel extreme learning machine (KELM) is a novel feedforward neural network,
which is widely used in classification problems. To some extent, it solves the
existing problems of the invalid nodes and the large computational complexity
in ELM. However, the traditional KELM classifier usually has a low test
accuracy when it faces multiclass classification problems. In order to solve
the above problem, a new classifier, Mexican Hat wavelet KELM classifier, is
proposed in this paper. The proposed classifier successfully improves the
training accuracy and reduces the training time in the multiclass
classification problems. Moreover, the validity of the Mexican Hat wavelet as a
kernel function of ELM is rigorously proved. Experimental results on different
data sets show that the performance of the proposed classifier is significantly
superior to the compared classifiers.
