| Summary: | With the continuous development of deep learning, its drawbacks are also beginning to appear. As an alternative to deep learning, broad learning is emerging. However, the level of broad learning model is shallow, so feature learning is not sufficient. In order to solve two problems of small samples whose dimensions are not very high in network model training, it cannot be adequately trained in the deep learning model and the features of input data cannot be fully learned in the broad learning model. This paper attempts to add a hidden layer on the enhancement nodes of the broad learning model and do shallow learning for the enhanced features to learn the hierarchical features again. This improved broad learning model also provides a new idea for solving the problem of small samples. From the perspective of regression and classification, this paper proves that for small samples whose dimensions are not very high, the effect of the improved broad learning model is better than that of the original broad learning model and also proves that the improved broad learning model has a good ability of application. This shows that the broad learning model based on enhanced features learning has the necessity and feasibility of further research.
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