1.Alternative view of logistic regression

Andrew Ng机器学习笔记week7 支持向量机SVM
逻辑回归:
Andrew Ng机器学习笔记week7 支持向量机SVM
SVM:
Andrew Ng机器学习笔记week7 支持向量机SVM

2.Large Margin Intuition

Andrew Ng机器学习笔记week7 支持向量机SVM
决策边缘:
Andrew Ng机器学习笔记week7 支持向量机SVM
可线性分离的情况:
Andrew Ng机器学习笔记week7 支持向量机SVM
存在异常值的情况:
Andrew Ng机器学习笔记week7 支持向量机SVM

3.kernel –核

非线性决策边缘:
Andrew Ng机器学习笔记week7 支持向量机SVM
核:
Andrew Ng机器学习笔记week7 支持向量机SVM
举例:
Andrew Ng机器学习笔记week7 支持向量机SVM
Andrew Ng机器学习笔记week7 支持向量机SVM
选择标志点:
Andrew Ng机器学习笔记week7 支持向量机SVM
SVM with Kernels:
Andrew Ng机器学习笔记week7 支持向量机SVM
SVM参数:
Andrew Ng机器学习笔记week7 支持向量机SVM

4.使用SVM
Andrew Ng机器学习笔记week7 支持向量机SVM
核函数:
Andrew Ng机器学习笔记week7 支持向量机SVM
核的选择:
Andrew Ng机器学习笔记week7 支持向量机SVM
多分类:
Andrew Ng机器学习笔记week7 支持向量机SVM
逻辑回归 VS SVM:
Andrew Ng机器学习笔记week7 支持向量机SVM

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