Heuristic Genetic Algorithm-Based Support Vector Classifier for Recognition of Remote Sensing Images

Chen, Zheng; Zheng, Guiwen; Jiao, Licheng

doi:10.1007/978-3-540-28647-9_103

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…In this paper, it is used as criterion function (FSV criterion function). fitness=� ((1-F)+ �) (5) N is the number of total samples, Ns is the number of support vector. FSV can depict more accurate real generalization error of SVM.…”

Section: Construct Criterion Functionmentioning

confidence: 99%

“…Currently, as an intelligent selection method of SVM model parameter, 978-1-4244-6585-9/10/$26.00 ©2010 IEEE 613 genetic algorithm (GA) [4] is widely used to achieve global optimization. In paper [5], genetic algorithm and Radius Margin generalization error bound are used to select SVM model parameter. It adequately makes use of global search ability of genetic algorithm and automatically selects the optimal parameter.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid optimization method for parameter selection of support vector machine

Xiao

Guo-yu

Song

et al. 2010

2010 IEEE International Conference on Intelligent Computing and Intelligent Systems

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Parameter selection of support vector machine (SVM) is a key problem in the application of SVM, which directly has influence on generalization performance of SVM.By using the support vector bound which is modified by F measure as criterion function and using genetic simulated annealing algorithm to select kernel parameters and penalty factor, a method of parameter selection of SVM based on hybrid optimization strategy is proposed in this paper. The proposed method adequately combines the optimization advantages of genetic algorithm and simulated annealing algorithms.The experiment results demonstrate that, compared with cross validation method, this proposed method improves accuracy of SVM parameter selection and generalization performance of SVM.

show abstract

Section: Construct Criterion Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid optimization method for parameter selection of support vector machine

Xiao

Guo-yu

Song

et al. 2010

2010 IEEE International Conference on Intelligent Computing and Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…Support Vector Machines (SVM), a more recent learning algorithm that has been developed from statistical learning theory [10,11], has a very strong mathematical foundation and has been shown to exhibit excellent performance in time series forecasting [7,[12][13][14] and in classification [15,16]. SVM is a new machine learning method based on the statistical learning theory, which solves the problem of over-fitting, local optimal solution and low convergence rate existed in ANN and has excellent generalization ability in the situation of small sample.…”

Section: Introductionmentioning

confidence: 99%

Parameters Optimization Using Genetic Algorithms in Support Vector Regression for Sales Volume Forecasting

Yuan

2012

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Budgeting planning plays an important role in coordinating activities in organizations. An accurate sales volume forecasting is the key to the entire budgeting process. All of the other parts of the master budget are dependent on the sales volume forecasting in some way. If the sales volume forecasting is sloppily done, then the rest of the budgeting process is largely a waste of time. Therefore, the sales volume forecasting process is a critical one for most businesses, and also a difficult area of management. Most of researches and companies use the statistical methods, regression analysis, or sophisticated computer simulations to analyze the sales volume forecasting. Recently, various prediction Artificial Intelligent (AI) techniques have been proposed in forecasting. Support Vector Regression (SVR) has been applied successfully to solve problems in numerous fields and proved to be a better prediction model. However, the select of appropriate SVR parameters is difficult. Therefore, to improve the accuracy of SVR, a hybrid intelligent support system based on evolutionary computation to solve the difficulties involved with the parameters selection is presented in this research. Genetic Algorithms (GAs) are used to optimize free parameters of SVR. The experimental results indicate that GA-SVR can achieve better forecasting accuracy and performance than traditional SVR and artificial neural network (ANN) prediction models in sales volume forecasting.

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Support vector classifier based on principal component analysis

Chen

Jiao

2008

J. of Syst. Eng. Electron.

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Heuristic Genetic Algorithm-Based Support Vector Classifier for Recognition of Remote Sensing Images

Cited by 4 publications

References 11 publications

Hybrid optimization method for parameter selection of support vector machine

Hybrid optimization method for parameter selection of support vector machine

Parameters Optimization Using Genetic Algorithms in Support Vector Regression for Sales Volume Forecasting

Support vector classifier based on principal component analysis

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