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High precision forecasting is a prerequisite and guarantee for the operation of grid-connected wind farms. Affected by various environmental factors, wind speed exhibits high fluctuations, autocorrelation and stochastic volatility. Therefore it remains great challenges for short-term wind speed forecasting. To capture its non-stationary property and its tendency, a forecasting model using support...
Based on the probability of memberships estimated by RVM (relevance vector machine) basic model, probabilistic output approaches for multi-class memberships in one-against-all strategy by multivariate sigmoid function and in one-against-one strategy by pairwise coupling are presented respectively. Experiment results based on artificial Gauss datasets and UCI datasets show the proposed approaches can...
As a cost function, fisher linear discriminant criterion can be used to optimize the kernel function. However, the dataset may not be linearly separable even after kernel transformation in many applications. So, SVMs that use the kernel function optimized by fisher criterion can not ensure the performance. Motivated by this issue, an ensemble algorithm was proposed. Firstly, partitioning a dataset...
Support vector machines (SVMs) have met with significant success in numerous real-world learning tasks. However, like most machine learning algorithms, SVMs is a supervised learning which is based on the assumption that it is straightforward to obtain labeled data, but in reality labeled data can be scarce or expensive to obtain. Active learning (AL) is a way to deal with the above problem by asking...
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