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Hyperspectral remote sensing image has the characteristics of hundreds of bands, high relevancy, and high redundancy, which bring difficulties to the further processing task. In order to reduce the dimension, this paper proposes an effective band selection method based on time series analysis of important points. The method takes all the continuous band of hyperspectral remote sensing images as time...
Chaotic behaviour has been shown to exist in financial data. This paper advances the use of the sparse kernel machine model for the prediction of directional change for this class of dynamical systems. The notions of low entropy trajectory sets and low entropy trajectory balls in phase space are defined as the building patterns for the predictor. The statistical stability and robustness of the sparse...
Exchange rate time series is often characterized as chaotic in nature. The prediction using conventional statistical techniques and neural network with back propagation algorithm, which is most widely applied, do not give reliable prediction results. Exchange-rate time series is also a dynamic non-linear system, whose characteristics cannot be reflected by the static neutral network. The Nonlinear...
Support vector machines, which are based on statistical learning theory and structural risk minimization principle, in theory, ensure the maximum generalization ability of the model. So compared with the neural network model established on the Empirical Risk Minimization principle, they are more comprehensive in theory. In this paper, it applies the support vector machine into building the time series...
Amyotrophic lateral sclerosis (ALS) is a type of neurological disease due to the degeneration of motor neurons. During the course of such a progressive disease, it would be difficult for ALS patients to regulate normal locomotion, so that the gait stability becomes perturbed. This paper presents a pilot statistical study on the gait cadence (or stride interval) in ALS, based on the statistical analysis...
Aim at the nonlinear forecasting of water resources time series, a SVM forecasting model based on chaotic state space reconstruction is established. The original time series is reconstructed to a high characteristic dimension space through nonlinear mapping so as to gain the input vector and anticipant output vector. The SVM model based on statistical learning theory is chosen for the forecasting...
Based on the powerful nonlinear mapping ability of support vector machines, the predicting model of support vector machines in combination with takens' delay coordinate phase reconstruction of chaotic time series has been established. Yearly precipitation time series is of the chaotic characters, thus this model is used to try predicting the precipitation. Because of the peculiarity of precipitation...
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