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Support Vector Machine (SVM), first proposed by Corinna Cortes and Vapnik in 1995, demonstrates unique advantages in solving small-sample, nonlinear, and high-dimensional pattern recognition problems. It can be extended to other machine learning tasks such as function fitting. In machine learning, SVM is a supervised learning model that analyzes data and recognizes patterns for classification and regression analysis. Key implementation aspects include kernel selection and margin optimization algorithms.

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(1) SVM is specifically designed for small sample problems, capable of obtaining optimal solutions with limited data samples; implementation involves kernel function selection and parameter optimization; (2) The SVM algorithm ultimately transforms into a quadratic programming problem that theoretically achieves global optimal solutions, contrasting with traditional neural networks' local optimum issues; the optimization process utilizes Lagrange multipliers and sequential minimal optimization (SMO) methods; (3) SVM's topological structure is determined by support vectors, avoiding the trial-and-error network structure determination required in traditional neural networks; this is implemented through support vector identification algorithms that automatically determine the model complexity.

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