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KPCA algorithm can solve the problem of nonlinear characteristic that the PCA algorithm can't handle with and the traditional curvelet decomposition algorithm cannot take full advantage of the fine scale component information. So we put forward KPCA algorithm and data fusion algorithm. The KPCA algorithm has a good effect on extracting face contour and the curve detail information through internal...
Extreme Learning Machine (ELM) for Single-hidden Layer Feedforward Neural Network (SLFN) has been attracting attentions because of its faster learning speed and better generalization performance than those of the traditional gradient-based learning algorithms. However, it has been proven that generalization performance of ELM classifier depends critically on the number of hidden neurons and the random...
DNA-binding proteins plays important role in a variety of vital biology processes. In this study, we apply a machine learning method for classify DNA-binding proteins from non-binding proteins based on sequence information. Using an evolutionary feature and residue composition feature extracted from primary structure, we have trained a support vector machine(SVM) to distinguish DNA-binding proteins...
Neural network has been widely used for nonlinear mapping, time-series estimation and classification. The unscented Kalman filter is a nonlinear parameter estimation algorithm. By means of it, weights update can be realized. In this paper a three layers neural network is used as a classification of the acupuncture EEG signals. The classifier directly classed the EEG instead of the feature values of...
Oil price time series is a nonlinear long-memory series, in this paper, a novel clustering method based on the symbiotic evolutionary and the immune programming algorithm is proposed, which is implemented for the prediction of oil price time series. In the design of the neural network, the number and positions of hidden layer are automatically adjust through symbiotic evolutionary and the immune programming...
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