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- In machine learning, the radial basis function kernel, or RBF kernel, is a popular kernel function used in various kernelized learning algorithms. In particular, it is commonly used in support vector machine classification. The RBF kernel on two samples and x', represented as feature vectors in some input space, is defined as may be recognized as the squared Euclidean distance between the two feature vectors. is a free parameter. An equivalent definition involves a parameter : Since the value of the RBF kernel decreases with distance and ranges between zero (in the limit) and one (when x = x'), it has a ready interpretation as a similarity measure.The feature space of the kernel has an infinite number of dimensions; for , its expansion using the multinomial theorem is: where , (en)
- 在机器学习中，（高斯）径向基函数核（英語：Radial basis function kernel），或称为RBF核，是一种常用的核函数。它是支持向量机分类中最为常用的核函数。 关于两个样本x和x'的RBF核可表示为某个“输入空间”（input space）的特征向量，它的定义如下所示： 可以看做两个特征向量之间的平方欧几里得距离。是一个自由参数。一种等价但更为简单的定义是设一个新的参数，其表达式为： 因为RBF核函数的值随距离增大而减小，并介于0（极限）和1（当x = x'的时候）之间，所以它是一种现成的表示法。核的特征空间有无穷多的维数；对于，它的展开式为： (zh)
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- 在机器学习中，（高斯）径向基函数核（英語：Radial basis function kernel），或称为RBF核，是一种常用的核函数。它是支持向量机分类中最为常用的核函数。 关于两个样本x和x'的RBF核可表示为某个“输入空间”（input space）的特征向量，它的定义如下所示： 可以看做两个特征向量之间的平方欧几里得距离。是一个自由参数。一种等价但更为简单的定义是设一个新的参数，其表达式为： 因为RBF核函数的值随距离增大而减小，并介于0（极限）和1（当x = x'的时候）之间，所以它是一种现成的表示法。核的特征空间有无穷多的维数；对于，它的展开式为： (zh)
- In machine learning, the radial basis function kernel, or RBF kernel, is a popular kernel function used in various kernelized learning algorithms. In particular, it is commonly used in support vector machine classification. The RBF kernel on two samples and x', represented as feature vectors in some input space, is defined as may be recognized as the squared Euclidean distance between the two feature vectors. is a free parameter. An equivalent definition involves a parameter : where , (en)
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- Radial basis function kernel (en)
- 径向基函数核 (zh)
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