Research on Image Classification of RBF Kernel SVM

Support Vector Machine (SVM), a robust machine learning algorithm, exhibits exceptional efficacy in addressing image multi-classification challenges. This paper aims to discuss the image multi-classification method based on RBF kernel SVM, and deeply analyze its principles, advantages, applications, and challenges. First of all, we will focus on the principle of support vector machine. SVM stands for Support Vector Machine, a powerful supervised learning algorithm that seeks to pinpoint a hyperplane within a highdimensional space that can separate different samples. By maximizing the interval, SVM can effectively process highdimensional data and make the classification boundary more robust. Subsequently, we will discuss the specific practice of SVM in image multi-classification applications. In many practical scenes, the task of image multi-classification requires the model to accurately divide multiple categories. By choosing the proper kernel function and adjusting parameters, SVM can achieve significant performance improvement in these scenarios. However, although SVM performs well in image multi-classification, it also faces some challenges. One of them is the computational complexity of large-scale data sets. With the increasing of image data, the traditional SVM algorithm may become time-consuming in training and prediction. Therefore, we adopt the strategy of RBF kernel SVM to improve the efficiency of SVM in multi-classification image data.