SVM Classification of IRIS Dataset with K-Fold Cross Validation

MATLAB implementation of Support Vector Machine (SVM) classification for IRIS dataset using K-fold cross-validation technique

Fuzzy Processing of Classification Samples Based on SVM Method

This program implements fuzzy processing of classification samples based on the Support Vector Machine (SVM) method, incorporating fuzzy factors to enhance classification precision through feature value adjustments.

The Fundamental Concept of SVM Method

The core idea of SVM method involves defining an optimal linear hyperplane and formulating the algorithm for finding this hyperplane as a convex optimization problem. Based on Mercer's kernel theorem, SVM employs a nonlinear mapping φ to transform the sample space into a high-dimensional (even infinite-dimensional) feature space (Hilbert space), where linear learning machines can effectively address highly nonlinear classification and regression problems from the original sample space. The SVM implementation typically involves coding support vector machine algorithms with key functions for kernel transformations and optimization solvers.