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 hidden units. We experimentally show that multi-activation hidden units can be used either to improve the classification accuracy, or to reduce computations.

中文翻译：

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具有随机权重的神经网络的多重激活隐藏单元

具有随机权重的单层前馈网络在各种分类和回归问题中取得了成功。这些网络以其非迭代和快速训练算法而闻名。这些网络的一个主要缺点是它们需要大量的隐藏单元。在本文中，我们建议使用多激活隐藏单元。这样的单元增加了可调参数的数量并能够形成复杂的决策面，而不会增加隐藏单元的数量。我们通过实验表明，多激活隐藏单元可用于提高分类精度或减少计算量。