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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,...
In this paper we propose a Round Trip Translation (RTT) based approach to sentence-level confidence estimation (CE) for spoken language translation without the assistant of reference translations generated by human. A number of novel RTT based features are introduced to reflect the quality of spoken language translation in more detail. After combing various kinds of features together, support vector...
Fuzzy support vector machine (FSVM) have been very successful in pattern recognition problems with outliers or noises. FSVM enhances the SVM in reducing the effect of noises in data points. In this paper, we introduce FSVM to regression problems for function approximation with noises. We apply a fuzzy membership to each input point of SVR and reformulate SVR into fuzzy SVR (FSVR) such that different...
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,...
A new method for pipe damage detection is presented based on the combination of finite element method (FEM) of B-spline wavelet on the interval (BSWI) and support vector regression (SVR). The pipe with various damage location and size is modeled by wavelet-based elements to gain precise frequencies for forward problem analysis. The SVR algorithm is employed to build the prediction and analysis model...
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,...
The great achievements have been approached in the development of support vector machine (SVM). It has been successfully used for solving classification and regression problems. This paper aims at proposing two algorithms based on SVC and SVR which are two applications of SVM in the fields of classification and regression, to handle both nominal and numerical missing values. Two experiments are conducted...
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...
Accurate traffic flow forecasting is key to the development of intelligent transportation systems (ITS). The support vector regression (SVR) method is employed for traffic flow forecasting and the comparative results between SVR and BP model using real traffic data of SCOOT system in Dalian city is also presented in this paper. Since support vector machines have better generalization performance and...
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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