Schlagwort: SVM

Article is in German!
Support Vector Machines (SVMs) offer a practical method for classifying multidimensional data, particularly when it cannot be cleanly separated by setting a threshold. The concept of soft margins and cross-validation is introduced to optimize the ratio of correctly and incorrectly classified data. The text further discusses the concept of hyperplanes and the capability of SVMs to transform low-dimensional data into higher dimensions for better classification. It also highlights the use of Scikit-Learn’s Polynomial-Kernel and Radial-Kernel functions for calculating support vectors in n-dimensional space.