论文标题
具有随机权重的神经网络的多激活隐藏单元
Multi-Activation Hidden Units for Neural Networks with Random Weights
论文作者
论文摘要
具有随机权重的单层馈电网络在各种分类和回归问题中都成功。这些网络以其非著作和快速培训算法而闻名。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表面，而无需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准确性或减少计算。
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.
