期刊论文、会议论文

Liu, Han

英文

PROCEEDINGS OF 2018 INTERNATIONAL CONFERENCE ON WAVELET ANALYSIS AND PATTERN RECOGNITION (ICWAPR)

2018

250-255

International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

International Conference on Wavelet Analysis and Pattern Recognition

JUL 15-18, 2018

Chengdu, PEOPLES R CHINA

[Zhao, Huihuang] Hengyang Normal Univ, Coll Comp Sci & technol, Hengyang 421008, Peoples R China.^[Liu, Han] Cardiff Univ, Sch Comp Sci & Informat, Queens Bldg,5 Parade, Cardiff CF24 3AA, Wales.^[Zhao, Huihuang] Hunan Prov Key Lab Intelligent Informat Proc & Ap, Hengyang 421002, Peoples R China.

Diee, Univ Alberta, Ulster Univ, IEEE, Portsmouth Univ, Univ Hyogo, IEEE Syst Man & Cybernet Soc, Univ Adelaide, Chengdu Univ, Univ Cagliari, Dept Elect & Electr Engn, Natl Key Lab Sci & Technol Blind Signal Proc

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

978-1-5386-5218-3

10.1109/ICWAPR.2018.8521321

National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61503128]; Science and Technology Plan Project of Hunan Province [2016TP102]; Scientific Research Fund of Hunan Provincial Education DepartmentHunan Provincial Education Department [14B025, 16C0311]; Hunan Provincial Natural Science FoundationNatural Science Foundation of Hunan Province [2017JJ4001]

本校为第一机构

Recognition of handwritten digits is a very popular application of machine learning. In this context, each of the ten digits (0-9) is defined as a class in the setting of machine learning based classification tasks. In general, popular learning methods , such as support vector machine, neural networks and K nearest neighbours, have been used for classifying instances of handwritten digits to one of the ten classes. However, due to the diversity of handwriting styles from different people, it can happen that some handwritten digits (e.g. 4 and 9...