新型工业化
新型工業化
신형공업화
New Industrialization Straregy
2014年
5期
48-53
,共6页
支持向量机%BP神经网络%分类%回归%泛化能力
支持嚮量機%BP神經網絡%分類%迴歸%汎化能力
지지향량궤%BP신경망락%분류%회귀%범화능력
support vector machine%BP neural network%classification%regression%generalization ability
支持向量机是一种基于统计学习理论的机器学习方法,由于其出色的学习性能,已经广泛应用于解决分类与回归问题。对比研究支持向量机和BP神经网络在分类与回归上的异同,通过仿真实验分析两者在测试集上分类与回归的泛化能力,研究表明支持向量机的泛化能力要优于BP神经网络。
支持嚮量機是一種基于統計學習理論的機器學習方法,由于其齣色的學習性能,已經廣汎應用于解決分類與迴歸問題。對比研究支持嚮量機和BP神經網絡在分類與迴歸上的異同,通過倣真實驗分析兩者在測試集上分類與迴歸的汎化能力,研究錶明支持嚮量機的汎化能力要優于BP神經網絡。
지지향량궤시일충기우통계학습이론적궤기학습방법,유우기출색적학습성능,이경엄범응용우해결분류여회귀문제。대비연구지지향량궤화BP신경망락재분류여회귀상적이동,통과방진실험분석량자재측시집상분류여회귀적범화능력,연구표명지지향량궤적범화능력요우우BP신경망락。
Support Vector Machine(SVM)is a new and pop machine-learning method based on statistical learning theory,widely used in solving classification and regression problems due to its excellent learning quality. In this paper,the support vector machines and BP neural network methods are under research. Based on the simulation experiment results on the classification and regression testing collection,the present SVM can obtain higher generalization ability when compared to another and the differences between them are also analyzed theoretically.