Support Vector Machine Classification - Breast Cancer Diagnosis Based on Electrical Impedance Characteristics of Breast Tissue

Application Context Support Vector Machine (SVM) is a novel machine learning method based on Statistical Learning Theory (STL) developed by Vapnik. STL employs the Structural Risk Minimization (SRM) principle, which minimizes both empirical error and structural risk to enhance model generalization capability without being constrained by data dimensionality. For linear classification, SVM positions the separating hyperplane to maximize the margin between two classes; for nonlinear classification, it transforms the problem into linear separation in high-dimensional space through kernel methods.

SVM Classification for Breast Cancer Diagnosis Based on Breast Tissue Electrical Impedance Properties

(1) SVM is specifically designed for small-sample problems, capable of obtaining optimal solutions with limited data samples; (2) The SVM algorithm ultimately transforms into a quadratic programming problem, theoretically yielding global optimal solutions and overcoming local optimality issues inherent in traditional neural networks; (3) SVM's topology is determined by support vectors, eliminating the trial-and-error approach required for determining network structures in traditional neural networks. The implementation involves optimizing margin constraints through convex optimization techniques.