A Survey of Techniques All Classifiers Can Learn from Deep Networks: Models, Optimizations, and Regularization
Deep neural networks have introduced novel and useful tools to the machine learning community, and other types of classifiers can make use of these tools to improve their performance and generality. This paper reviews the current state of the art for deep learning classifier technologies that are being used outside of deep neural networks. Many components of existing deep neural network architectures can be employed by non-network classifiers. In this paper, we review the feature learning, optimization, and regularization methods that form a core of deep network technologies. We then survey non-neural network learning algorithms that make innovative use of these methods to improve classification. We conclude by discussing directions that can be pursued to expand the area of deep learning for a variety of classification algorithms.