Proposition of Kernel principal component analysis based Quantum-behaved Particle swarm optimization algorithm of Support Vector Machines (KQP-SVM) Algorithm. The CP-SVM algorithm can acquire higher precision but with lower speed.
2.提出基于核主成分分析的量子行为粒子群优化支持向量机预测算法(KQP-SVM)CP-SVM预测算法可以得到较高的预测精度,但预测速度较慢。
参考来源 - 智能优化支持向量机预测算法及应用研究In order to overcome the shortcomings of conventional Kernel Principal Component Analysis(KPCA) method in modeling and analyzing of large sample data(e. g. , high computational complexity, long time modeling and large storage space etc.
常规核主元分析(KPCA)方法在对大样本数据分析建模时，存在运算复杂度高、建模时间长以及所需存储空间大等缺点。
参考来源 - 基于相似度的核主元分析方法及其应用研究The kernel projection analysis, including the kernel principal component analysis (KPCA) and kernel Fisher discriminant analysis (KFDA), is an efficient nonlinear feature extraction method proposed by Scholkopf and Mike et al recently.
核投影分析，包括核主分量分析(KPCA)和核Fisher鉴别分析(KFDA)，是最近刚刚提出的非常有效的非线性特征抽取方法。
参考来源 - 基于核投影分析的特征抽取及应用研究In view of Principal Component Analysis (PCA) in the breakdown characteristic choice insufficiency, has introduced the kernel method, realized one kind to be effective based on the Kernel Principal Component Analysis (KPCA) non-linear characteristic choice method.
3、研究并实现了基于核主元分析的特征选择方法。
参考来源 - 基于支持向量机的旋转机械故障诊断方法研究In this paper, a supervised fuzzy clustering RBF neural network (SFCM-RBFNN) based on kernel principal component analysis is introduced for constructing the garment seam evaluation system. Experimental results demonstrate that the proposed system could efficiently evaluate the fabric sewing ability.
本文针对这些问题,在对面料的FAST力学性能数据进行相关分析和核主成分分析的基础上,提出基于监督模糊聚类的径向基神经网络(SFCM-RBFNN)来客观评价服装缝纫性能,取得了较好的预测效果。
参考来源 - 基于神经计算的服装缝纫性能模糊评价研究·2,447,543篇论文数据，部分数据来源于NoteExpress
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The nonlinear components of gait features are extracted based on kernel principal component analysis (KPCA).
在训练阶段，核-主元分析用来捕捉非线性的手写变化。
An approach to gear fault diagnosis is presented, which bases on kernel principal component analysis (KPCA).
提出了基于核函数主元分析的齿轮故障诊断方法。
In the training phase, kernel principal component analysis is used to capture nonlinear handwriting variations.
在训练阶段，核-主元分析用来捕捉非线性的手写变化。
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