K-Fold and Stochastic Search-Based Optimization for Hyperbolic Support Vector Machine

With the widespread application of machine learning, the accuracy and generalization capability of models have become central research issues. Support Vector Machines (SVMs) are extensively used in fields such as pattern recognition, image processing, and data mining; however, their performance is heavily dependent on the selection of hyperparameters. Traditional manual tuning methods are time-consuming and prone to local optima. To address this, this paper proposes an optimization model for Hyperbolic Support Vector Machines (HSVM) based on K-fold cross-validation and Random Search. Kfold cross-validation is employed to assess the model's generalization ability, while Random Search enhances optimization efficiency by randomly selecting hyperparameter combinations. Experimental results demonstrate that the KRS Hyperbolic SVM model significantly outperforms traditional SVMs in terms of classification accuracy, stability, and generalization ability.