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Khan, Atlas; Yang, Jie; Wu, Wei
Neurocomputing (Amsterdam), 03/2014, Letnik: 128Journal Article, Conference Proceeding
A learning scheme based on Extreme Learning Machine (ELM) and L1/2 regularization is proposed for a double parallel feedforward neural network. ELM has been widely used as a fast learning method for feedforward networks with a single hidden layer. A key problem for ELM is the choice of the (minimum) number of the hidden nodes. To resolve this problem, we propose to combine the L1/2 regularization method, that becomes popular in recent years in informatics, with ELM. It is shown in our experiments that the involvement of the L1/2 regularizer in DPFNN with ELM results in less hidden nodes but equally good performance.
