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翻译scikit-learn Cookbook

学习sklearn

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Classifying data with support vector machines支持向量机用于分类数据

kernel 机器学习神经网络深度学习

Support vector machines (SVM) is one of the techniques we will use that doesn't have an easy probabilistic interpretation. The idea behind SVMs is that we find the plane that separates the group of the dataset the "best". Here, separation means that the choice of the plane maximizes the margin between the closest points on the plane. These points are called support vectors.

到不了的都叫做远方

2019-12-01

4930

Kernel PCA for nonlinear dimensionality reduction核心PCA非线性降维

kernel 数据分析对象存储

Most of the techniques in statistics are linear by nature, so in order to capture nonlinearity,we might need to apply some transformation. PCA is, of course, a linear transformation.In this recipe, we'll look at applying nonlinear transformations, and then apply PCA for dimensionality reduction.

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2019-11-02

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