Corpus ID: 221802368

Multi-Activation Hidden Units for Neural Networks with Random Weights

@article{Patrikar2020MultiActivationHU,
  title={Multi-Activation Hidden Units for Neural Networks with Random Weights},
  author={A. Patrikar},
  journal={ArXiv},
  year={2020},
  volume={abs/2009.08932}
}

• A. Patrikar
• Published 2020
• Computer Science
• ArXiv

Single layer feedforward networks with random weights are successful in a variety of classification and regression problems. These networks are known for their non-iterative and fast training algorithms. A major drawback of these networks is that they require a large number of hidden units. In this paper, we propose the use of multi-activation hidden units. Such units increase the number of tunable parameters and enable formation of complex decision surfaces, without increasing the number of… Expand

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