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Echo state networks (ESNs), that exhibit good performance for modeling a nonlinear or non-Gaussian dynamic system, have been widely used for time series prediction. However, estimating the output weights of the ESNs remains intractable. Extended Kalman filter (EKF) is an effective estimate method, but its computational cost is relatively high. In this study, a Map Reduce framework based parallel zed...
Prediction intervals that provide estimated values as well as the corresponding reliability are applied to nonlinear time series forecast. However, constructing reliable prediction intervals for noisy time series is still a challenge. In this paper, a bootstrapping reservoir computing network ensemble (BRCNE) is proposed and a simultaneous training method based on Bayesian linear regression is developed...
SMS has become an indispensable tool in people's life. How to help people effectively anti spam SMS and create a healthy, harmonious and ordered environment has become a new research hotspot. On the basis of learning Bayesian learning theory, this thesis mainly researches Bayesian classification model and Bayesian decision based on the minimum risk.
The problem of ranking has recently gained attention in data learning. The goal ranking is to learn a real-valued ranking function that induces a ranking or ordering over an instance space. In this paper, we apply popular Bayesian techniques on ranking support vector machine. We propose a novel differentiable loss function called trigonometric loss function with the desirable characteristic of natural...
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