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Software project development is a risky process with high failure rate. This paper proposes an intelligent model that can predict and control software development risks from an overall project perspective rather than focusing only on the single factor, project output. In this study, we first constructed a formal model for risk identification, and then collected actual cases from software development...
The fouling of heat exchanger is an unsolved difficult problem in all over the world. The research on the fouling prediction of heat exchanger is significantly to improve operational efficiency and economic benefits of the plants. The application of Support Vector Machine (SVM) based on Statistical Learning Theory to predict heat exchanger fouling was introduced, and the Genetic Algorithm (GA) was...
Under the opening economic circumstances, forecasting the risks of capital flow has special significance. For effectively early warning the risks associated with capital flow, this study applies support vector machine (SVM) to the domain of capital flow in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, a grid-search technique using 5-fold cross-validation...
Support vector machine is a new machine learning technique developed on the basis of statistical learning theory, which has become the hotspot of machine learning because of its excellent learning performance. Based on analyzing the theory of support vector machine for regression (SVR), a SVR model is established for predicting the output in fully mechanized mining face, and then realizes the model...
Spontaneous Combustion in Coal Seam (SCCS) is seriously threatening coal mine safety. A novel approach to predict SCCS by using Support Vector Machine (SVM) is present. The SVM is based on statistical learing theory with a simple structure and good generation properties. The basic SVM principle was firstly reviewed. Then, the kernel function was choiced, and the model parameters were optimized with...
Global prediction techniques such as support vector machines show accurate prediction for time series data; however, such models tend to delay the predicted output. Fuzzy systems have benefits in local optimum, thus producing significant results within training sets. Unfortunately, the existing techniques sometimes give undesired effects of surface oscillation at predicted outputs. This paper presents...
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