Support vector machine (SVM) is the minimization of structural risk to construct a better hyperplane to maximize the distance between the hyperplane and the simple points on both sides of hyperplane. Two improved phys...
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Support vector machine (SVM) is the minimization of structural risk to construct a better hyperplane to maximize the distance between the hyperplane and the simple points on both sides of hyperplane. Two improved physics-wise swarm intelligence optimizationalgorithms (Henryl gassolubilityoptimizationalgorithm and Archimedes optimisation algorithm) were proposed based on Levy flight operator, Brownian motion operator and Tangent flight motion operator to optimize the penalty factor and kernel function parameters of SVM so as to enhance its global and local search ability. Iinally, the Iris datasets, Strip surface defect datasets, Wine datasets and Wisconsin datasets of breast cancer in UCh datasets were selected to carry out the simulation experiment. Sinulation results show that optimizing SVM based on improved physical-wise swarm intelligence algorithms can effectively improve the classification accuracy.
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