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Usually, traditional machine learning (ML) methods use only labeled samples for learning. However, in practical problems including electromagnetic optimization design, the acquisition cost of labeled samples is relatively high. Obtaining label training samples is the most time‐consuming part, so how to use relatively few label samples for training to obtain a high‐precision surrogate model is a hot...
A synthesis model based on support vector machine ensemble (SVME) is proposed for the direction of arrival (DOA) estimation. This method is based on the advantages of the support vector machine (SVM), such as high fitting precision, simple structure, and strong generalization ability. The basic concept of the method is to select SVMs, optimally, to construct an SVME with the aid of the binary particle‐swarm...
Gauss process (GP) is a learning machine which has developed rapidly in recent years. Compared with the methods of artificial neural network and support vector machine, GP is easy to implement and has the advantages of adaptive acquisition and predictive output. This paper presents a modeling method based on GP. When constructing the proposed GP model, the input samples of the model calculates the...
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