基于支持向量机的棋盘数据分类研究
Research on Chessboard Data Classification Based on Support Vector Machine

本文旨在探索支持向量机(SVM)在棋盘数据分类中的应用效果及其性能，特别是在国际象棋和围棋等棋类游戏的局面分类问题上。通过对不同参数设置下的SVM模型进行实验，本文分析了线性核、多项式核及径向基函数(RBF)核SVM在处理高维、复杂棋局数据时的准确率和泛化能力。本文对比了多种SVM模型在棋盘数据上的分类性能，通过交叉验证和细致的参数调优过程，选出了最优模型。实验结果表明，SVM模型尤其是采用RBF核的模型，在棋盘数据分类任务中展示出了显著的性能优势，包括高准确率和良好的泛化能力。此外，实验也揭示了特征选择和模型参数调优在提高分类性能中的重要性。
This paper aims to explore the application effect and performance of support vector machine (SVM) in chessboard data classification, especially in the situation classification of chess and go. Through experiments on SVM models with different parameter settings, the accuracy and generalization ability of linear kernel, polynomial kernel and radial basis function (RBF) kernel SVM in processing high-dimensional and complex chess data are analyzed in this study. In this paper, the classification performance of multiple SVM models on chessboard data is compared, and the optimal model is selected through cross-validation and meticulous parameter tuning process. The experimental results show that the SVM model, especially the model with RBF kernel, shows significant performance advantages in chessboard data classification tasks, including high accuracy and good generalization ability. In addition, the experiment also reveals the importance of feature selection and model parameter tuning in improving classification performance.