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Training Optimization of Feedforward Neural Network for Binary Classification

机译:二元分类前馈神经网络的训练优化

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In this paper, we present a heuristic based technique to reduce the training time of a feedforward neural network by intuiting some of the parameters involved in construction and initialization of the network. These estimated parameters include the number and size of the hidden layers along with the weights related to the neurons. The weights and network architecture is estimated before training by formulating a geometric approximation of the target function we want the network to learn. This specific configuration will allow the network to learn the optimum weights in less iterations than in the case of random initialization of weights.
机译:在本文中,我们提出一种基于启发式的技术,通过直观了解一些参与网络构建和初始化的参数来减少前馈神经网络的训练时间。这些估计的参数包括隐藏层的数量和大小以及与神经元有关的权重。权重和网络架构是在训练之前通过制定我们希望网络学习的目标函数的几何近似值来估计的。与权重的随机初始化的情况相比,这种特定的配置将允许网络以更少的迭代来学习最佳权重。

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