摘要
超参数的选择直接影响着支持向量机(SVM)的泛化性能和回归效验,是确保SVM优秀性能的关键。针对超参数穷举搜索方法的难点,从试验设计的角度,提出了正交设计超参选择方法,并分析了基于混合核函数(比单一核函数具有更好的收敛性和模型适应性)SVM各个超参数的取值范围,选定了每个参数的试验水平。通过考虑参数间的正交性和交互性,选取最优超参数组合下的SVM模型。应用该方法,对两种典型滑坡位移时序的SVM建模进行了超参数组合正交优化设计,获得了精度高且泛化性能良好的滑坡预测模型,其试验结果验证了方法的可靠性。正交设计超参选择方法较之其他超参选择法简单实用,其高时效的特点更有助于SVM在实践工程中的良好应用。
Selection of the hyper-parameters is critical to the performance of support vector machines （SVM）, directly impacting the generalization and regression efficacy of the SVM. An orthogonal experimental design procedure for hyper-parameter selection （ODPS） is clearly desirable given the intractable problem of exhaustive search methods. The authors＇ previous work in this area involved analyzing the range value of hyper-parameters for SVM of mixed kernel which has been proved and showed a higher convergence rate and a greater flexibility in learning a problem space than single kernel functions, and determining experimental levels for different parameters in order to guide the hyper-parameter selection process. The method selects hyper-parameters optimal composition in terms of orthogonal and interaction effect of hyper-parameters. The results of the performed engineering experiments for the prediction of two typical landslide deformation time series confirmed the reliability and advantage of the proposed approach.
出处
《岩土力学》
EI
CAS
CSCD
北大核心
2010年第2期503-508,515,共7页
Rock and Soil Mechanics
基金
西部交通建设科技项目(No.200331880201)
关键词
正交设计
支持向量机
超参数
时序回归
滑坡
orthogonal design
support vector machines
hyper-parameters
time series regression
landslide
