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A Project risk forecast model was investigated using least square support vector machine(LS-SVM) method. Risk estimation data of experts was acted as eigenvector of learning samples to train the constructed LS-SVM regression model for realizing mapping relationship between the risk and the characteristic. The test samples were used to compare between the constructed LS-SVM model and BP neural network...
In order to overcome the problem that the least square support vector machines (LS-SVM) using Gaussian kernel cannot approximate arbitrary signal with multi-scale, a scaling ker-nel for LS-SVM is proposed. LS-SVM can be used simultaneously to approximate to the target function and improve the effectiveness of generalization and approximation in the local area model. The LS-SVM with scaling kernel...
Method of support vector machine (SVM) as a new machine learning algorithm has shown its superiority of the ability of regression in the fields of damage identification. Through setting variation displacement of mode shape to the feature parameters of damage identification, the method of the damage identification of long-span cable-stayed bridge based on SVM is presented. The method of least square...
A number of different forecasting methods have been proposed for cooling load forecasting including historic method, real-time method, time series analysis, and artificial neural networks (ANN), but accuracy and time efficiency in prediction are a couple of contradictions to be hard to resolve for real-time traffic information prediction. In order to improve time efficiency of prediction, a new hourly...
Stock return forecast has been an important issue and difficult task for both shareholders and financial professionals. To tackle this problem, we introduce least square support vector machine (LS-SVM), an improved algorithm that regresses faster than standard SVM, and dynamic inertia weight particle swarm optimization (W-PSO), that outperform standard PSO in parameter selection. The work of this...
Time series prediction is a main research content in time series analysis, and has become a hot research field with great theoretical value and application value. As an extension type of least square support vector machine (LS-SVM), recurrent LS-SVM is proposed and applied to chaotic time series prediction. Aimed at the key and difficult research problem on LS-SVM - the selection and construction...
Stock yield forecast has been an important issue and difficult task for both shareholders and financial professionals. In this paper, we introduce least square support vector machine (LS-SVM), an improved algorithm that regresses faster than standard SVM, and the parameters of model proposed are gained in the three levels of Bayesian inference. The work of this paper is as following: First, forecast...
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