Recently, support vector machine (SVM) has shown a better performance than conventional learning methods in many applications. This paper proposes a weighted ...
Abstract. The problem of determining optimal decision model is a difficult combinatorial task in the fields of pattern classification, machine learning, and.
Genetic algorithms has been shown to be effective in systematically finding optimal kernel parameters for SVM, instead of randomly selected kernel parameters.
Nov 25, 2020 · Genetic algorithm–optimized SVM for activity recognition The SVM model has four hyperparameters: kernel function, penalty coefficient, kernel ...
Abstract Support Vector Machines (SVMs) are widely known as an efficient supervised learning model for classifi- cation problems. However, the success of an ...
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The optimization of parameters for the SVM kernel function is achieved through the Improved Whale Optimization Algorithm, by tuning the kernel function's ...
Sep 25, 2018 · In order to feed the more effective features into the SVM model, GA was proposed to select the most model-effective PCs for the corresponding ...
Jul 9, 2021 · Connecting GA to SVM allows us to estimate optimal values of SVM's adjustable parameters in the shortest time and increase predicting accuracy.
SVM Parameter Optimization with Python: A Step-by-Step Guide
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Apr 16, 2023 · In SVM, the parameter optimization problem can be formulated as finding the optimal hyperplane that maximizes the margin between the two classes ...
In the present study, a brand new method is developed and introduced as GA–SVM with parameter optimization. This method benefits from support vector machine ( ...