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In this paper, the car sales prediction model is established by using Support Vector Regression (SVR) combined with Particles Swarm Optimization algorithm (PSO-SVR). In this model, PSO Algorithm is used to optimize the 3 parameter used in Support Vector Regression. PSO algorithm not only has a strong global search capability, but also solved the problem of over-fitting. Moreover, Mean Absolute Percentage...
Evaluation of construction projects is an important task for management of construction projects. An accurate forecast is required to enable supporting the investment decision and to ensure the project's feasible at the minimal cost. So controlling and rationally determining the construction cost plays the most important roles in the budget management of the construction project. Ways and means have...
Support Vector Machines for Regression (SVR) proved to perform well. However, they are not preferred in image analysis due to a high number of needed support vectors (SV) and consequently long processing times. We present a method for simplifying the original SVR regression function up to a user-specified degree of accepted performance decrease. We show results for two regression problems: modelling...
The support vector machine (SVM), proposed by Vapnik (1995), has been successfully applied to classification, cluster, and forecast. This study proposes support vector regression (SVR) to forecast real estate prices in China. The aim of this paper is to examine the feasibility of SVR in real estate price prediction. To achieve the aim, five indicators are selected as the input variables and real estate...
In this paper, by using support vector machine (SVM) regression, the simulation model of the influences of stakeholders on capital management in commercial banks is established. The results are tested by utilizing the samples of Chinese joint-stock commercial banks during the period of 1999-2006 and compared with results of PLS regression and BP neural network. The result is gotten that the forecasting...
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