LIB-SVM Structure Parameters, Kernel Functions, SVM Prediction, and Training

LIB-SVM is a widely-used support vector machine toolkit whose core functionality can be divided into four key components: structural parameter configuration, kernel function selection, model training, and prediction. The following analysis examines its core logic from a technical implementation perspective: In terms of structural parameters, LIB-SVM encapsulates key parameters through the svm_parameter structure. This includes SVM types (such as C-SVC/nu-SVC), kernel function types (linear/polynomial/RBF), penalty coefficient C, kernel-related parameters (like gamma value for RBF), and termination conditions. These parameters directly affect the model's convergence and generalization capabilities. In code implementation, developers typically set these parameters using the svm_parameter struct before initiating the training process. As core components of SVM, LIB-SVM implements four typical kernel functions: the linear kernel directly computes vector dot products; the polynomial kernel controls complexity through the degree parameter; the RBF kernel (Gaussian kernel) relies on the gamma parameter to adjust nonlinearity; and the sigmoid kernel suits specific scenarios. Kernel function selection should consider data characteristics and problem complexity. The kernel type is specified in the svm_parameter struct using constants like LINEAR, POLY, RBF, or SIGMOID. The training process employs the Sequential Minimal Optimization (SMO) algorithm, which decomposes large-scale quadratic programming problems into smaller subproblems for iterative solving. LIB-SVM constructs decision functions by computing kernel matrices between support vectors. Training results are stored in the svm_model structure, which contains support vector sets, coefficient arrays, and decision function parameters. The main training function svm_train() handles this process efficiently. During prediction, the trained model is loaded to compute kernel function values between new samples and each support vector. After weighted summation, classification results or regression values are obtained through the decision function. For multi-class problems, LIB-SVM defaults to a one-vs-one strategy that constructs multiple binary classifiers. The prediction phase is typically implemented using the svm_predict() function. Optimization recommendations include: selecting optimal parameter combinations through cross-validation, paying attention to the relationship between RBF kernel's gamma value and feature dimensions, and considering cache mechanisms to optimize kernel matrix calculation efficiency for large-scale data. Cross-validation can be implemented using LIB-SVM's built-in grid.py script or custom cross-validation routines.