Forward Thinking：构建深度随机森林
Forward Thinking: Building Deep Random Forests
摘要：
深度神经网络的成功激发了许多人思考其他学习者是否可以从深度分层架构中受益。我们提出了一个称为深度学习前瞻性思维的一般框架，它概括了深度神经网络的架构灵活性和复杂性，同时还允许（i）网络中除神经元之外的不同类型的学习功能，以及（ii）根据需要自适应地深化网络以改善结果的能力。这是通过一次训练一个层来完成的，一旦训练了一个层，输入数据就会通过该层向前映射，以创建一个新的学习问题。然后重复该过程，通过多个层转换数据，一次一个，渲染一个新的数据集，该数据集预计会表现得更好，并且最终输出层可以实现良好的性能。在用决策树替换深度神经网络的神经元的情况下，我们将结果称为前瞻性思考深度随机森林（FTDRF）。我们通过在MNIST数据集上应用FTDRF来证明概念。我们还提供了一个通用的数学公式，允许考虑其他类型的深度学习问题。
英文摘要：
The success of deep neural networks has inspired many to wonder whether other learners could benefit from deep, layered architectures. We present a general framework called forward thinking for deep learning that generalizes the architectural flexibility and sophistication of deep neural networks while also allowing for (i) different types of learning functions in the network, other than neurons, and (ii) the ability to adaptively deepen the network as needed to improve results. This is done by training one layer at a time, and once a layer is trained, the input data are mapped forward through the layer to create a new learning problem. The process is then repeated, transforming the data through multiple layers, one at a time, rendering a new dataset, which is expected to be better behaved, and on which a final output layer can achieve good performance. In the case where the neurons of deep neural nets are replaced with decision trees, we call the result a Forward Thinking Deep Random Forest (FTDRF). We demonstrate a proof of concept by applying FTDRF on the MNIST dataset. We also provide a general mathematical formulation that allows for other types of deep learning problems to be considered.
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