乳腺癌诊断 Resources

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A random forest, as the name suggests, constructs a forest using random methods, comprising numerous decision trees where each tree operates independently without correlation. After the forest is built, when a new input sample arrives, each decision tree in the forest individually classifies the sample. The final prediction is determined by majority voting, selecting the class with the highest frequency among all trees. This ensemble approach enhances classification robustness and reduces overfitting through bootstrap aggregation (bagging) and random feature selection during tree construction.

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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.

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(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.

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