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Multiple kernel methods are superior to single kernel methods on treating multiple, heterogeneous data sources. Different from the existing multiple kernel methods which mainly work in implicit kernel space, we propose a novel multiple kernel method in empirical kernel mapping space. In empirical kernel mapping space, the combination of kernels can be treated as the weighted fusion of empirical kernel...
Because of fuzzy and correlative parameters of maintainability design, which are the factors to influent the maintainability design, traditional evaluating methods of maintainability design fail to distinctly express the influence of design factors on maintainability. This paper applies a method linking fuzzy mathematic theory and neural network to establish a network for analyzing the parametric...
In this paper, a joint source-channel coding (JSCC) scheme for unequal error protection (UEP) based SPIHT-coded image transmission over wireless channels is proposed. SPIHT-coded image information is divided into different substreams according to their different importance. Each substream is transmitted differentially over MIMO system based on MIMO adaptive channel assignment policy (ACAP) to achieve...
This paper presents a new wrapper-based feature selection method for multi-layer perceptrons (MLP) neural networks. It uses a feature ranking criterion to measure the importance of a feature by computing the aggregate difference, over the feature space, of the probabilistic outputs of the MLP with and without the feature. Thus, a score of importance with respect to every feature can be provided using...
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...
Load demand prediction is vital for maintaining stability and controlling risks of electricity market. An improved model which combines neural network with genetic algorithm is proposed to accurately predict load demand at equilibrium situation of day-ahead electricity market. In the proposed model, load demand prediction problem is converted into optimization problem of error minimization between...
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