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Support vector regression (SVR) is a common learning method for machines which is developed these years. Comparing with the other regression models, this method has the advantages of structural risk minimization and strong generalization ability. It is widely used in the prediction field and acquires good effects. The training characters of SVR model are very important to SVR. To solve the problem,...
Gait is a well recognized biometric feature that is used to identify a human at a distance. However, in real environment, appearance changes of individuals due to viewing angle changes cause many difficulties for gait recognition. This paper re-formulates this problem as a regression problem. A novel solution is proposed to create a View Transformation Model (VTM) from the different point of view...
In recent years, Support Vector Regression (SVR) is used widely in predication field, with the advantages of structural risk minimization and strong generalization ability, which acquires good effects. The training characters of SVR model is the essential problem of affecting model accuracy. To solve the problem, this paper puts forward SVR model training method based on wavelet multi-resolution analysis,...
Short term load forecasting is very essential to the operation of electricity companies. However, the methods of complexity of training time and space can not be acceptable when using a large dataset for forecasting a period of power loads. This paper proposes a new method for short term load forecasting using particle swarm optimization (PSO) and Core Vector Regression (CVR), PSO is applied for determining...
A new heterogeneous catalysis modeling methodology, namely support vector regression (SVR) and chaotic particle swarm optimization algorithm (CPSO) was presented, for catalyst compositional models and catalytic reaction mechanism models, for reducing both high temporal costs and financial costs, and accelerating the process of industrialization synthesis of dimethyl ether (DME). In the SVR-CPSO approach,...
This study applies a novel neural-network technique, support vector regression (SVR), to predict reliably in dynamical system. The aim of this study is to examine the feasibility of SVR in state prediction by comparing it with the existing neural-network approaches. To build an effective SVR model, SVR's parameters must be set carefully. This study proposes a novel approach, known as GA-SVR, which...
This paper presents a method for the identification of Hammerstein models based on support vector regression (SVR). First, the intermediate linear model was established through converting the nonlinear equations of Hammerstein to a class of linear one by the function expansion. Second, training samples for intermediate linear model were obtained by operating measured data synthetically, and coefficients...
It is important to choose good parameters in support vector regression (SVR) modeling. Choosing different parameters will influence the accuracy of SVR models. This paper proposes a parameter choosing method of SVR models for time series prediction. In the light of data features of time series, the paper improves the traditional cross-validation method, and combines the improved cross-validation with...
How to extract rules from trained SVMs has become an important preprocessing technique for data mining, pattern classification, and so on. There are two key problems required to be solved in the SVM based classification rule extraction, i.e. the attribute selection and the discretization to continuous attributes. In this paper, the differential characteristic of SVR (Support vector regression) is...
Training a support vector regression (SVR) resumes to the process of migrating the vectors in and out of the support set along with modifying the associated thresholds. This paper gives a complete overview of all the boundary conditions implied by vector migration through the process. The process is similar to that of training a SVM, though the process of incrementing / decrementing of vectors into...
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