Earticle

초록

영어

Handwritten digit recognition is a task of great importance in many applications. There are different challenges faced while attempting to solve this problem. It has drawn much attention from the field of machine learning and pattern recognition. Minimax probability machine (MPM) is a novel method in machine learning and data mining. In this paper, we present an extension algorithm for MPM, which is named twin minimax probability machine (TWMPM). TWMPM generates two hyperplanes to improve the classification accuracy. Experiment results on several data sets from the UCI repository demonstrate that TWMPM can improve performance of MPM in most cases. The proposed method is used for recognizing the handwritten digits provided in the MNIST data set of images of handwritten digits (0-9). The testing accuracies are improved comparing with MPM.

목차

Abstract
 1. Introduction
 2. Feature Extraction
 3. TWMPM
  3.1. Minimax Probability Machine (MPM)
  3.2. Twin Support Vector Machine (TWSVM)
  3.3. TWMPM
 4. Experiments
  4.1. UCI Datasets
  4.2. MNIST Data Set
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Zhijie Xu [ School of Science, Beijing University of Civil Engineering and Architecture, Beijing 102616, China ]
• Jianqin Zhang [ Urban Geomatics of National Administration of Surveying, Mapping and Geoinformation, Beijing University of Civil Engineering and Architecture, Beijing 100044, China ]
• Hengyou Wang [ School of Science, Beijing University of Civil Engineering and Architecture, Beijing 102616, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605