Handwritten digit recognition via active belief decision trees
Ziyi Li, Liyao Ma, Xiaolu Ke, Yong Wang · 2016
Considering uncertainty within the learning methods is attracting more and more attentions in recent years, yet not much work has been done when it comes to the area of handwritten digit recognition. In this paper, the active belief decision tree is used to handle the epistemic uncertainty in the practical application. The detailed feature extraction method for belief decision trees is proposed and discussed. Under the conditions such as lack of knowledge about true labels, few instances to train the model and low-dimensional image features, we still achieve a good classification performance as shown in experiments with data from the commonly used MNIST handwritten digits benchmark.