搜索资源列表
KPCA
- KPCA是一种基于核的主要成分分析，是一种由线性到非线性之间的桥梁。通过非线性函数把输入空间映射到高维空间，在特征空间中间型数据处理，引入核函数，把非线性变换后的特征空间内积运算转换为原始空间的核函数计算。 基本思想是通过某种隐士方法将输入空间映射到某个高维空间（特征空间），并在特征空间实现PCA。对该算法进行了详细的说明-KPCA is a kernel-based principal components analysis, is a bridge between the linear
SVC-and-SVR
- 基于SVM数据分类及回归分析，并采用不同的核函数如RBF,sigmoid,polynomial等-the data classification and regression analysis based on SVM, by using different kinds of kernel functions, for examples, RBF,sigmoid and ploynomial and so on
kernelICA-jmlr
- We present a class of algorithms for independent component analysis (ICA) which use contrast functions based on canonical correlations in a reproducing kernel Hilbert space. On the one hand, we show that our contrast functions are related to mutu
kde
- kernel density estimation based on fast marching method
Bayesian Efficient Multiple Kernel Learning
- 基于贝叶斯核学习的文档，在分类以及判断类别方面有帮助(Based on Bayesian Nuclear Learning)