A New Approach to Detect Splice-Sites Based on Support Vector Machines and a Genetic Algorithm

In this paper, a new approach for segmentation and recognition of Persian handwritten numbers is presented. This method utilizes the framing feature technique in combination with outer profile feature that we named this the adapted framing feature. In our proposed approach, segmentation of the numbers into digits has been carried out automatically. In the classification stage of the proposed method, Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN) are used. Experimentations are conducted on the IFHCDB database consisting 17,740 numeral images and HODA database consisting 102,352 numeral images. In isolated digit level on IFHCDB, the recognition rate of 99.27%, is achieved by using SVM with polynomial kernel. Furthermore, in isolated digit level on HODA, the recognition rate of 99.07% is achieved by using SVM with polynomial kernel. The experiments illustrate that applying our proposed method resulted higher accuracy compared to previous researches.

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Identifying Travel Mode with GPS Data Using Support Vector Machines and Genetic Algorithm

Information ◽

10.3390/info6020212 ◽

2015 ◽

Vol 6 (2) ◽

pp. 212-227 ◽

Cited By ~ 8

Author(s):

Fang Zong ◽

Yu Bai ◽

Xiao Wang ◽

Yixin Yuan ◽

Yanan He

Keyword(s):

Genetic Algorithm ◽

Support Vector Machines ◽

Support Vector ◽

Gps Data ◽

Travel Mode ◽

Vector Machines

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Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm

Journal of Zhejiang University SCIENCE A ◽

10.1631/jzus.2005.b0961 ◽

2005 ◽

Vol 6B (10) ◽

pp. 961-973 ◽

Cited By ~ 17

Author(s):

Yong Mao ◽

Xiao-bo Zhou ◽

Dao-ying Pi ◽

You-xian Sun ◽

Stephen T.C. Wong

Keyword(s):

Genetic Algorithm ◽

Support Vector Machines ◽

Gene Selection ◽

Gaussian Kernel ◽

Support Vector ◽

Parameters Selection ◽

Vector Machines ◽

Kernel Support Vector Machines

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Patent Innovation Factors Evolution Based on P-SVM and GCCA

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.39.247 ◽

2010 ◽

Vol 39 ◽

pp. 247-252

Author(s):

Sheng Xu ◽

Zhi Juan Wang ◽

Hui Fang Zhao

Keyword(s):

Genetic Algorithm ◽

Support Vector Machines ◽

Correlation Coefficient ◽

Network Architecture ◽

Innovation Management ◽

Model Development ◽

Real Data ◽

Support Vector ◽

Vector Machines ◽

Gray Correlation

A two-stage neural network architecture constructed by combining potential support vector machines (P-SVM) with genetic algorithm (GA) and gray correlation coefficient analysis (GCCA) is proposed for patent innovation factors evolution. The enterprises patent innovation is complex to conduct due to its nonlinearity of influenced factors. It is necessary to make a trade off among these factors when some of them conflict firstly. A novel way about nonlinear regression model with the potential support vector machines (P-SVM) is presented in this paper. In the model development, the genetic algorithm is employed to optimize P-SVM parameters selection. After the selected key factors by the PSVM with GA model, the main factors that affect patent innovation generation have been quantitatively studied using the method of gray correlation coefficient analysis. Using a set of real data in China, the results show that the methods developed in this paper can provide valuable information for patent innovation management and related municipal planning projects.

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