Feature Selection and SVM Parameter Synchronous Optimization Based on a Hybrid Intelligent Optimization Algorithm

Amid the rapid development of science and technology and the increasingly fierce market competition, low cost coupled with high performance is the key to a company's competitiveness. This requires optimizing all the links in the actual output. Hybrid intelligent algorithms can combine the advantages of different algorithms to solve a large number of optimization problems in engineering practice. Therefore, this study is focused on a hybrid swarm intelligence algorithm and its application. As a classic method of machine learning, the kernel function, which is based on the support vector machine (SVM) and the selection of the parameters in the kernel function, has an important influence on the performance of the classifier. The use of kernel function technology cannot only greatly reduce the amount of calculation in the input space, but can also effectively improve machine learning classification performance. In the field of machine learning, choosing and building the core functions is a notable difficulty. However, little research has been conducted in this area so far. In view of the above problems, this study discusses and analyzes the structure of the support frame machine core in detail, and improves the traditional parameter optimization algorithm. It also proposes a new method of fuzzy clustering algorithm automatic parameter learning combined with the basic ideas of a genetic algorithm in order to improve the parameter optimization strategy of support vector regression, so as to obtain better prediction results. Through simulation experiments, the improved hybrid core SVM and parameter optimization algorithm were applied to the ORL face database, greatly improving the recognition rate, and experiments were carried out after adding noise to the images in the face database to verify the practicability and practicality of the algorithm. The robustness and reliability of the algorithm were improved by at least 30%, thus confirming the feasibility of the proposed algorithm.