Application research of robust LS-SVM regression model in forecasting patent application counts

Li Wei Zhang; Qian Zhang; Xue Feng Wang; Dong Hua Zhu

Application research of robust LS-SVM regression model in forecasting patent application counts

Li Wei Zhang^*, Qian Zhang, Xue Feng Wang, Dong Hua Zhu

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

A forecasting system of patent application counts is studied in this paper. The optimization model proposed in the research is based on support vector machines (SVM), in which cross-validation algorithm is used for preferences selection. Results of data simulation show that the proposed method has higher forecasting precision power and stronger generalization abi1ity than BP neural network and RBF neural network. In addition, it is feasible and effective in forecasting patent application counts.

Original language	English
Pages (from-to)	497-501
Number of pages	5
Journal	Journal of Beijing Institute of Technology (English Edition)
Volume	18
Issue number	4
Publication status	Published - Dec 2009

Keywords

Cross-validation algorithm
Forecasting
Patent application count
Support vector machine

Cite this

@article{47bd550643ab432bb233261e6057f208,

title = "Application research of robust LS-SVM regression model in forecasting patent application counts",

abstract = "A forecasting system of patent application counts is studied in this paper. The optimization model proposed in the research is based on support vector machines (SVM), in which cross-validation algorithm is used for preferences selection. Results of data simulation show that the proposed method has higher forecasting precision power and stronger generalization abi1ity than BP neural network and RBF neural network. In addition, it is feasible and effective in forecasting patent application counts.",

keywords = "Cross-validation algorithm, Forecasting, Patent application count, Support vector machine",

author = "Zhang, {Li Wei} and Qian Zhang and Wang, {Xue Feng} and Zhu, {Dong Hua}",

year = "2009",

month = dec,

language = "English",

volume = "18",

pages = "497--501",

journal = "Journal of Beijing Institute of Technology (English Edition)",

issn = "1004-0579",

publisher = "Beijing Institute of Technology",

number = "4",

}

TY - JOUR

T1 - Application research of robust LS-SVM regression model in forecasting patent application counts

AU - Zhang, Li Wei

AU - Zhang, Qian

AU - Wang, Xue Feng

AU - Zhu, Dong Hua

PY - 2009/12

Y1 - 2009/12

N2 - A forecasting system of patent application counts is studied in this paper. The optimization model proposed in the research is based on support vector machines (SVM), in which cross-validation algorithm is used for preferences selection. Results of data simulation show that the proposed method has higher forecasting precision power and stronger generalization abi1ity than BP neural network and RBF neural network. In addition, it is feasible and effective in forecasting patent application counts.

AB - A forecasting system of patent application counts is studied in this paper. The optimization model proposed in the research is based on support vector machines (SVM), in which cross-validation algorithm is used for preferences selection. Results of data simulation show that the proposed method has higher forecasting precision power and stronger generalization abi1ity than BP neural network and RBF neural network. In addition, it is feasible and effective in forecasting patent application counts.

KW - Cross-validation algorithm

KW - Forecasting

KW - Patent application count

KW - Support vector machine

UR - http://www.scopus.com/inward/record.url?scp=75349092827&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:75349092827

SN - 1004-0579

VL - 18

SP - 497

EP - 501

JO - Journal of Beijing Institute of Technology (English Edition)

JF - Journal of Beijing Institute of Technology (English Edition)

IS - 4

ER -

Application research of robust LS-SVM regression model in forecasting patent application counts

Abstract

Keywords

Other files and links

Fingerprint

Cite this