An adaptive deep Q learning strategy for handwritten digit recognition
Handwritten digits recognition is a challenging problem in recent years. Although many deep learning based classification algorithms are studied for handwritten digits recognition, the recognition accuracy and running time still need to be further improved. In this paper, an adaptive deep Q learning strategy is proposed to improve accuracy and shorten running time for handwritten digit recognition. The adaptive deep Q learning strategy combines the feature extracting capability of deep learning and the decision making of reinforcement learning to form an adaptive Q learning deep belief network (Q ADBN) First, Q ADBN extracts the features of original images using an adaptive deep auto encoder (ADAE) and the extracted features are considered as the current states of Q learning algorithm. Second, Q ADBN receives Q function (reward signal) during recognition of the current states, and the final handwritten digits recognition is implemented by maximizing the Q function using Q learning algorithm. Finally, experimental results from the well known MNIST dataset show that the proposed Q ADBN has a superiority to other similar methods in terms of accuracy and running time.
Neural Networks