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Support vector machines (SVMs) were initially proposed to solve problems with two classes. Despite the myriad of schemes for multiclassification with SVMs proposed since then, little work has been done for the case where the classes are ordered. Usually one constructs a nominal classifier and a posteriori defines the order. The definition of an ordinal classifier leads to a better generalisation....
In this paper, fire detection technology based on multi-sensors information fusion is presented for the complexity of fire process and the multiplicity of fire environment. And support vector machines are used to establish fire detecting model of information fusion, for Support vector machines show excellent performance in generalization and optimization. The improved genetic algorithm by Auto-adaptive...
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...
Precise forecasting for shareprice is very important to investment and financing. Support vector regression, called as SVR, is a novel learning algorithm based on statistical learning theory, which has greater generalization ability than traditional neural networks. In order to select the appropriate parameters of SVR, particle swarm optimization is introduced to choose the user-determined parameters...
To improve the generalization ability of the machine learning and solve the problem that recognition rates of the speech recognition system become worse in the noisy environment, a modified Gaussian kernel function which may pay attention to the similar degree between sample space and feature space is proposed. In this paper, used the modified Gaussian kernel support vector machine to a speech recognition...
A new prediction model that combining the merits of support vector machine (SVM) and gray RBF neutral network is proposed in this paper. First apply structural risk minimization principle to optimize the modeling method of RBF neutral network, so that the radial basis centers and network weights could be acquired directly. Then use error compensator of RBF neutral network based on structural risk...
In this paper, combining the auto-correlation wavelet kernel with multiclass least squares support vector machine (MLS-SVM), a novel notion of multiclass least squares support vector machine with universal auto-correlation wavelet kernels (MLS- AWSVM) is proposed. The translation invariant property of the kernel function enhances the generalization ability of the LS-SVM method and the spiral multiclass...
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