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In China stock market, more than 95% are non-professional investors. Due to the lack of professional skill and the complexity of financial indicators and the varying investment environment, non-professional investors are in great need of a data mining-based intelligent stock trading decision-support system. Considering the existence of concept drift phenomenon, this study proposes an adaptive learning...
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...
Least squares support vector machine (LSSVM) has been used in soft sensor modeling in recent years. In developing a successful model based on LSSVM, the first important step is feature extraction. Principal components analysis (PCA) is a usual method for linear feature extraction and kernel PCA (KPCA) is a nonlinear PCA developed by using the kernel method. KPCA can efficiently extract the nonlinear...
Mooney-viscosity is the dominate quality index for synthetic rubber. Monitoring the Mooney-viscosity effectively and realizing automatic quality control of the production process is an urgent problem in the rubber industry. This paper proposes a soft sensor model based on PCA-LSSVM to predict the Mooney-viscosity of styrene butadiene rubber (SBR). First, major parameters affecting the Mooney-viscosity...
Software project development has high failure rate. Software project risk management may gain a high rate of return in investment. Establishing an intelligent risk evaluation model for project will be valuable in the analysis and control of project risks. In this paper, we employed neural network (NN) and support vector machine (SVM) approaches to establish a model for risk evaluation in project development...
Support vector machine (SVM), which based on statistical learning theory, is a universal machine learning method. The fault diagnosis of nonlinear and high-controllable high voltage direct current (HVDC) system based on SVM method is proposed, which can take full advantage of effective ability and superiority of SVM in dealing with small samples, and solve many familiar problems in fault diagnosis...
Even the multiple support vector machine (SVM) ensemble has been proved to improve the classification performance greatly than a single SVM, the classification result of the practically implemented SVM is often far from the theoretically expected level. As compared to traditional bagging and boosting methods, this paper proposes a novel SVM ensemble method based on fuzzy integral and rough reducts...
Even the support vector machine (SVM) has been proved to improve the classification performance greatly than a single SVM, the classification result of the practically implemented SVM is often far from the theoretically expected level because they don't evaluate the importance degree of the output of individual component SVMs classifier to the final decision. This paper proposes a boosting least square...
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