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Deep neural networks have introduced novel and useful tools to the machinelearning community. Other types of classifiers can potentially make use ofthese tools as well to improve their performance and generality. This paperreviews the current state of the art for deep learning classifier technologiesthat are being used outside of deep neural networks. Non-network classifierscan employ many components found in deep neural network architectures. In thispaper, we review the feature learning, optimization, and regularization methodsthat form a core of deep network technologies. We then survey non-neuralnetwork learning algorithms that make innovative use of these methods toimprove classification. Because many opportunities and challenges still exist,we discuss directions that can be pursued to expand the area of deep learningfor a variety of classification algorithms.

A Survey of Techniques All Classifiers Can Learn from Deep Networks: Models, Optimizations, and Regularization