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Relevance vector machine (RVM) is a state-of-the-art technique for regression and classification, as a sparse Bayesian extension version of the support vector machine. The selection of a kernel and associated parameter is a critical step of RVM application. The real-world application and recent researches have emphasized the requirement to multiple kernel learning, in order to boost the fitting accuracy...
Relevance vector machine (RVM) is a state-of-the-art technique for regression and classification, as a sparse Bayesian extension version of the support vector machine. The kernel function and parameter selection is a key problem in the research of RVM. The real-world application and recent researches have emphasized the requirement to multiple kernel learning. This paper proposes a novel regression...
Selecting a suitable kernel for relevance vector machine is one of most challenging aspects of successfully using this learning tool. Efficiently automating the search for such a kernel is therefore desirable. This paper proposes a data-driven kernel function construction and optimization method, which combines genetic programming (GP) and relevance vector regression to evolve an optimal or near-optimal...
Shock isolator with minimum settling time (SIMST) is a vital device in military and civil use. At present, the design of SIMST draws a lot of attention. Because one of the indexes of SIMST is time variable other than state variable, there is no computation method given so far, and the design of SIMST mainly depends on experiments. In this paper, genetic algorithm is applied to the design. The genetic...
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