Yu Chen
Northeast Forestry University

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

The Color Extraction and Support Vector Machine Recognition Algorithm for Moving Plate Recognition System Yu Chen; Jun Cao; Aifei Wang
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 7: July 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

According to the shortcomings of long time and big errors about the moving plate recognition system, we present the moving plate recognition algorithm based on color extraction and support vector machine. On the basis of the analysis of moving plate recognition system’s basic principles, it introduces the basic principles and calculation steps about color extraction and support vector machine algorithm, and discusses the feasibility of applying the algorithm to PRS in the paper. The experimental results show that the algorithm has the advantages of faster speed and higher accuracy of recognition. The algorithm provides a new thought for the research on the moving plate recognition algorithm. DOI: http://dx.doi.org/10.11591/telkomnika.v11i7.2800
Based on Weighted Gauss-Newton Neural Network Algorithm for Uneven Forestry Information Text Classification Yu Chen; Liwei Xu
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 5: May 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In order to deal with the problem of low categorization accuracy of minority class of the uneven forestry information text classification algorithm, this paper puts forward the uneven forestry information text classification algorithm based on weighted Gauss-Newton neural network, on the basis of weighted Gauss-Newton algorithm, the algorithm is proved via singular value decomposition principle. The experimental result shows that the algorithm has higher classification accuracy of majority class and minority class than algorithm of common classification. The algorithm expands a new method for the research on the uneven forestry information text classification algorithm. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.4388