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Related Experiment Video

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Novel Object Recognition Test for the Investigation of Learning and Memory in Mice
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An adaptive deep Q-learning strategy for handwritten digit recognition.

Junfei Qiao1, Gongming Wang1, Wenjing Li1

  • 1Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 9, 2018
PubMed
Summary

This study introduces an adaptive deep Q-learning strategy for handwritten digit recognition, enhancing accuracy and reducing processing time. The novel Q-ADBN model outperforms existing methods on the MNIST dataset.

Keywords:
Adaptive Q-learning deep belief networkAdaptive deep auto-encoderDeep learningHandwritten digits recognitionReinforcement learning

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Handwritten digit recognition remains a challenge, with existing deep learning methods requiring improvements in accuracy and efficiency.
  • Current algorithms struggle to balance recognition performance with computational speed.

Purpose of the Study:

  • To propose an adaptive deep Q-learning strategy to enhance handwritten digit recognition accuracy and reduce running time.
  • To introduce a novel model, the Q-ADBN, integrating deep learning and reinforcement learning.

Main Methods:

  • Developed an adaptive Q-learning deep belief network (Q-ADBN) combining deep learning and reinforcement learning.
  • Utilized an adaptive deep auto-encoder (ADAE) for feature extraction, defining states for the Q-learning algorithm.
  • Employed Q-learning to maximize the Q-function for final digit recognition, using reward signals.

Main Results:

  • The Q-ADBN demonstrated superior performance compared to existing methods on the MNIST dataset.
  • Achieved significant improvements in both recognition accuracy and reduced running time.

Conclusions:

  • The proposed Q-ADBN effectively enhances handwritten digit recognition by integrating deep feature extraction with reinforcement learning decision-making.
  • The strategy offers a promising approach for improving the efficiency and accuracy of digit recognition systems.