CAJ | 학술논문

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.
초삼수적선택직접영향착지지향량궤(SVM)적범화성능화회귀효험,시학보SVM우수성능적관건.침대초삼수궁거수색방법적난점,종시험설계적각도,제출료정교설계초삼선택방법,병분석료기우혼합핵함수(비단일핵함수구유경호적수렴성화모형괄응성)SVM각개초삼수적취치범위,선정료매개삼수적시험수평.통과고필삼수간적정교성화교호성,선취최우초삼수조합하적SVM모형.응용해방법,대량충전형활파위이시서적SVM건모진행료초삼수조합정교우화설계,획득료정도고차범화성능량호적활파예측모형,기시험결과험증료방법적가고성.정교설계초삼선택방법교지기타초삼선택법간단실용,기고시효적특점경유조우SVM재실천공정중적량호응용.
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.