In this paper, a novel type of feed-forward neural network with a simple
structure is proposed and investigated for pattern classification. Because
the novel type of forward neural network?s parameter setting is mirrored
with those of the Extreme Learning Machine (ELM), it is termed the mirror
extreme learning machine (MELM). For the MELM, the input weights are
determined by the pseudoinverse method analytically, while the output
weights are generated randomly, which are completely different from the
conventional ELM. Besides, a growing method is adopted to obtain the optimal
hidden-layer structure. Finally, to evaluate the performance of the proposed
MELM, abundant comparative experiments based on different real-world
classification datasets are performed. Experimental results validate the
high classification accuracy and good generalization performance of the
proposed neural network with a simple structure in pattern classification.
A New Probabilistic Output Constrained Optimization Extreme Learning Machine
