Titlebook: Weak Convergence and Empirical Processes; With Applications to Aad W. Vaart,Jon A. Wellner Book 19961st edition Springer Science+Business M

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Aad W. van der Vaart,Jon A. Wellnerators in the oscillator network are trained, whereas in the . stage, the weights of the feedforward network are trained using the complex backpropagation algorithm. The Functional Connectivity Matrix (FCM) obtained from the network’s output is compared with empirical Functional Connectivity Matrix,

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Aad W. van der Vaart,Jon A. Wellnerderived from the Hilbert transform and time-frequency maps, information-theoretic divergence measures in time domain and time-frequency domain, state space based measures (in particular, non-linear interdependence measures and the S-estimator), and at last, the recently proposed stochastic-event syn

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Aad W. van der Vaart,Jon A. Wellnered by an optimization method based on the minimal-redundancy-maximal-relevance criterion. Moreover, the relationship between the simplified features and human gaits is modeled by using a random forest algorithm, based on which the patient-specific gait trajectories can be predicted. Finally, the per

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Aad W. van der Vaart,Jon A. Wellnerreserve richer structure details than that in spatial domain. The experimental results on three standard image datasets show that our proposed method is capable of obtaining higher PSNR/SSIM values and preserving complex edges and curves better than other state-of-the-art methods.

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