有指导机器学习超参数的交叉验证智能优化

Intelligent Optimization of Hyper-Parameters of Supervised Machine Learning Based on Cross Validation

不合理的模型超参数会导致有指导机器学习过拟合或欠拟合,文中提出模型超参数的交叉验证智能优化方法:利用交叉验证评价不同参数下的学习性能,在此基础之上构建智能寻优技术路线.实验结果表明:利用交叉验证性目标函数,智能优化算法自适应地根据目标函数和约束求解,可高效、准确地优化学习器超参数.

Unreasonable hyper-parameters lead the supervised machine learning to over-fitting or under -fitting. The paper presents a cross validation intelligent optimization of hyper-parameters. The cross validation is used to evaluate learning performance under the different parameters, on the basis of which the technical path of intelligent optimization is determined. Experimental results show that, by using the cross validation objective function,the intelligent optimization algorithm finds the best hyper-parameters efficiently and accurately according to the objective function and constraints adaptively.