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Support vector machines (SVM) is a widely used method which can treat problems involving small sample, devilish learning, and high dimension. The current paper conduct a multivariate SVM in a total-factor production framework, and the GDP per capita, capital stock and labor are taken as the independent variables and the energy consumption is the dependent variable. The Gaussian radial basis function...
Motivated by the great success of dynamic time warping (DTW) in time series matching, Gaussian DTW kernel had been developed for support vector machine (SVM)-based time series classification. Counter-examples, however, had been subsequently reported that Gaussian DTW kernel usually cannot outperform Gaussian RBF kernel in the SVM framework. In this paper, by extending the Gaussian RBF kernel, we propose...
In this paper, we propose a hybrid framework that first uses an adapted Gaussian mixture model based method to represent a varying length sequence of feature vectors as a fixed length pattern and then uses a discriminative model for classification of varying length patterns of long duration. In the conventional GMM-UBM (Gaussian mixture model-Universal background model) based classifier, a UBM is...
This paper analyzes the impact of different detrending approaches on the performance of a variety of computational intelligence (CI) models. Three approaches are compared: Linear, nonlinear detrending (based on empirical mode decomposition) and first-differencing. Five representative CI methods are evaluated: Dynamic evolving neural-fuzzy inference system (DENFIS), Gaussian process (GP), multilayer...
This paper proposes a new probabilistic method for maximum temperature forecasting in short-term electrical load forecasting. The proposed method makes use of Gaussian process (GP)of the kernel machine to evaluate the predicted temperature. In recent years, electric power markets become more deregulated and competitive. The power system players are concerned with maximizing a profit while minimizing...
Modeling time series data of varying length is important in different domains. There are two paradigms for modeling the varying length sequential data. Tasks such as speech recognition need modeling the temporal dynamics and the correlations among the features. Hidden Markov models (HMM) are used for these tasks. In tasks such as speaker recognition, audio classification and speech emotion recognition,...
This paper investigates the applicability of Gaussian processes (GP) classification for recognition of articulated and deformable human motions from image sequences. Using tensor subspace analysis (TSA), space-time human silhouettes (extracted from motion videos) are transformed to low-dimensional multivariate time series, based on which structure-based statistical features are calculated to summarize...
We propose a simple formulation for regularized kernel function approximation. The regression function is obtained by minimizing an unconstrained quadratic function. Further reduced kernel technique is also employed in its formulation which enables it to handle large database. The solution of this objective function is obtained by solving a system of linear equations and thus no need for any quadratic...
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