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Long-term time series prediction is to predict the future values multi-step ahead. It has received more and more attention due to its applications in predicting stock prices, traffic status, power consumption, etc. In this paper, a k-nearest neighbors (k-NN) based least squares support vector machine (LS-SVM) framework is proposed to perform long-term time series prediction. A new distance function,...
This paper addressed multivariate calibration based on least square support vector machines (LS-SVM) regression to provide a powerful model for machine learning and data mining. LS-SVM technique have the advantages to provide the capability of learning a high dimensional feature with fewer training data, and to decrease the computational complexity for requiring only solving a set of linear equation...
A hip protector system using an airbag for prevention of femoral neck fractures is under developing by our group. In the system, instance detection of falling motions by using an appropriate on-line algorithm based on sensor signals is required. The purpose of the present paper is to propose online distinction procedures for human falling motions based on the machine learning, such as the support...
A new neural network for least squares support vector machines (LS-SVM) learning, which combines LS-SVM with recurrent neural networks, is proposed based on the learning network of standard SVM. It is obtained using Lagrange multipliers directly which eliminates the nonlinear parts of the standard SVM learning network. The proposed network can be used for classification and regression application,...
As a powerful machine learning approach for pattern recognition problems, support vector machine is known to have good generalization ability. Based on the index system of enterprise's self-fulfillment capability, a new integrated evaluation model is established by using support vector regression method. The method has advantages of accuracy, convenience, reliability and rapidity. The method is illustrated...
An adaptive and iterative LSSVR algorithm based on quadratic Renyi entropy is presented in this paper. LS-SVM loses the sparseness of support vector which is one of the important advantages of conventional SVM. The proposed algorithm overcomes this drawback. The quadratic Renyi entropy is the evaluating criterion for working set selection, and the size of working set is determined at the process of...
A new method was proposed for incorporating prior knowledge in the form of fuzzy knowledge sets into Support Vector Machine for regression problem. The prior knowledge of Fuzzy IF-THEN rules can be transformed into fuzzy information to generate fuzzy kernel, based on which FSVR (Fuzzy Support Vector Regression) is introduced. The merit of FSVR is that it can incorporate with prior knowledge represented...
We describe an ensemble of classifiers based approach for incrementally learning from new data drawn from a distribution that changes in time, i.e., data obtained from a nonstationary environment. Specifically, we generate a new classifier using each additional dataset that becomes available from the changing environment. The classifiers are combined by a modified weighted majority voting, where the...
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