Paper Titles
The Application of Second Development Technology for SolidEdge in Web-Based Parts Library
p.814
Virtual Assembly and Kinematic Simulation of Gear Form Grinding Machine Based on Pro/E
p.819
The Intelligent Design of the Gear Reducer
p.823
Study on Key Technologies of Web Parts Library System Development
p.828
A Decision Tree Method Based on Support Vector Machineto Identify Egg Embryo
p.833
Research on Loads Control of Wind Turbine Based on Disturbance Accommodating Control
p.838
Assessment of Implementation Capability for Design-Manufacturing Integration Based on Support Vector Machine
p.842
Application of Ug Re-Development Technology in 3-D Parameterized Modeling of Injection Mould Parts
p.847
Research on RFID-Based Production Data Aggregation Technology
p.853

A Decision Tree Method Based on Support Vector Machineto Identify Egg Embryo

Article Preview

Abstract:

This paper mainly studies a decision tree method based on support vector machine to identify egg embryo. It analyses the main characteristics of the types of egg embryo and sets up a kind of multilayer decision tree classifier by using "solution space". And it figures out the correct rate of the decision tree classifier, which concentrates on the types of egg embryo. By introducing the support vector machine (SVM) algorithm based on the structure of the binary tree for multi-class classification, it identifies different kinds of egg embryo. Not only does this method make the decision capability of the optimal one in every level of the decision tree, but also assures the overall optimal performance of the whole decision tree, and effectively improves the correct recognition rate of the decision tree classifier about the types of egg embryo.

You might also be interested in these eBooks
Progress in Advanced Manufacturing Technologies View Preview

Info:

Periodical:

Key Engineering Materials (Volume 522)

Pages:

833-837

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.522.833

Citation:

Cite this paper

Online since:

August 2012

Authors:

Tan Cheng Xie, Heng Xuan Mao, Yan Wei Xu, Xiang Nang, Xiao Jing Hu

Keywords:

Decision Tree Classifier, Egg Embryo, Support Vector Machine (SVM)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

References

[1] J.W. Tian and M.J. Gao: The Research and Application of the Artificial Neural Network Algorithm ( Beijing Institute of Technology Press, China 2006), pp.227-238. ( In Chinese).

Google Scholar

[2] N. Zhang, J.J. Liu and Z.J. Liu: Ship Science and Technology, Vol. 28 (2006) No. 5, pp.93-98. (In Chinese).

Google Scholar

[3] Z.Y. Hu, T.C. Xie, X. Nan and B. Ren: Machinery Design and Manufacture, (2010) No. 10, pp.81-83. (In Chinese).

Google Scholar

[4] J.P. Marques de Sa and Y.F. Wu: Pattern Recognition-Concepts, Methods and Applications (Tsinghua University Press, China 2003), pp.115-130. (In Chinese).

Google Scholar

[5] F.M. Tang, Z.D. Wang and M.Y. Chen: Control and Decision, Vol. 20 (2005) No. 7, pp.746-749. (In Chinese).

Google Scholar

[6] S.X. Du and T.J. Wu: Journal of Zhejiang University (Engineering Science), Vol. 37 (2003) No. 5, pp.521-527. ( In Chinese).

Google Scholar