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Hydrology time series prediction is significant. It is not only helpful to set the planning in daily configuration works of water resources, but also provides guidance for leaders to make decision, especially in some special case such as flood and seriously lack water. In order to solve the imbalance complexity of prediction model and complexity of samples and raise forecasting accuracy, combined...
Bacterial meningitis is still a life-threatening disease, and early diagnosis of pathogen can be crucial to improving survival rate. Using the surface-enhanced Raman scattering (SERS) platform developed by our group, the pathogens can be differentiated on the basis of their SERS spectra which are believed to related to their surface chemical components. We collected the SERS spectra of ten pathogens:...
The county level of basic public services analysis and classification play an important role in county economic growth and improve benefit of healthy development of urbanization in China. According to the county level of basic public services data which is large scale and imbalance, this paper presented a support vector machine model to classify the county level of basic public services. The method...
A multi-kernel Support Vector Machine model, called Hierarchical Support Vector Regression (HSVR), is proposed here. This is a self-organizing (by growing) multiscale version of a Support Vector Regression (SVR) model. It is constituted of hierarchical layers, each containing a standard SVR with Gaussian kernel, at decreasing scales. HSVR have been applied to a noisy synthetic dataset. The results...
Relevance vector machine (RVM) is a state-of-the-art technique for regression and classification, as a sparse Bayesian extension version of the support vector machine. The selection of a kernel and associated parameter is a critical step of RVM application. The real-world application and recent researches have emphasized the requirement to multiple kernel learning, in order to boost the fitting accuracy...
Relevance vector machine (RVM) is a state-of-the-art technique for regression and classification, as a sparse Bayesian extension version of the support vector machine. The kernel function and parameter selection is a key problem in the research of RVM. The real-world application and recent researches have emphasized the requirement to multiple kernel learning. This paper proposes a novel regression...
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...
In order to ensure safety in coal production, full assurance is given for fully-mechanized excavated faces. Based on the vector supporting machine for regression (SVR), a model is established for predicting the gas emission in fully-mechanized excavated faces. The index system is analyzed and the model parameters are chosen. Then, the sample set of gas emission in fully-mechanized coal driving workface...
Syndrome is a unique TCM concept, which is an abstractive collection of symptoms and signs. Several modern algorithms have been applied to classify syndromes, but no satisfied results have been obtained because of the complexity of diagnosis procedure. Support vector machine (SVM) has been found to be very efficient to solve the classification problems, especially for binary classification with good...
Cellular Automata (CA) are dynamic mathematical systems (which are discrete in time and space, operate in uniform regular lattice, and are characterized by local interaction) that can be used with GIS to simulate land use change. Common cellular automata calibration is based on linear logistic regression. Linear models, however, are susceptible to over-fitting; especially when applied to nonlinearly...
The support vector machine (SVM) is an algorithm based on structure risk minimizing principle, having high generalization ability. In the course of multi-sensor information fusion of industrial control, sensor has bigger nonlinearity and fuzzy relation between coefficient and relevant parameter. A kind of model and algorithm of multiple sensor information fusion based on the support vector machine...
[Purpose] Mass incidents have emerged as a serious social problem concerning national security in China. So, it is necessary to construct a forecasting model to predict such public events. In this paper, support vector machines are applied to the model. [Method] Based on the social surveys conducted in 119 counties of Shanxi, Gansu and Hubei provinces, 3 multi-class classification problems were proposed,...
In this work we analyze the application of Support Vector Machines for Regression (SVRs) to the problem of identifying weakly nonlinear systems. Examples of simple linear and nonlinear systems are considered, taking into account both non-recursive and recursive models. When defining the SVR estimating function, several kinds of kernels are employed, and the effect on the accuracy performance of reducing...
In this paper, we use support vector machine (SVM) and artificial neural networks to diagnosis hepatitis diseases. Furthermore, we use those networks to identify the type and the phase of disease. Considering the most important hepatitis cases leads us to six classes: hepatitis B (two phases), hepatitis C (two phases), non-viral hepatitis and no-hepatitis. For this purpose, we design various networks...
Based on the statistical learning theory support vector machine focuses on the machine learning strategies under small samples and gets better generalization ability than those tools based on the experience risk minimization principle. Its classing or regression performance will be affected by relative super-parameters. An improved multi object optimization algorithm based on simulated annealing is...
Based on the SVM characteristics of not depending on the prior knowledge of the solved problem and the Features of small sample calculation, this paper will establish a combined bias model of network RTK by Nu-SVR regression model of the SVM. This paper experiments on the artificial of the model, and gets some rules about these parameters. Finally, the paper establishes a better combined bias model...
This paper introduces a general Bayesian framework for obtaining sparse solutions to regression predicting, and the practical model 'relevance vector machine' (RVM) by Michael E. Tipping. As a brand-new thought of probabilistic learning model, it offers the superior level of generalization accuracy and a number of additional advantages comparable with the popular and state-of-the-art 'support vector...
By comparing performance of common kernels and wavelet kernels in classification, criterion of effective kernel for support vector classifier is concluded, thereby a tight support kernel is constructed by smoothing Shannon scaling function in Fourier domain and combining with spline function. Experiment results indicate that the proposed kernel has faster training speed and higher accuracy than Gaussian...
This paper presents a new SVM algorithm framework optimized by PSO algorithm. The value of parameters in the SVM has great influence on the performance of regression model. In previous works the choice of these parameters mainly depends on the experience. In our work PSO algorithm was used to optimize these parameters to form a new SVM framework - PSVM. The proposed algorithm was used to forecast...
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