利用Logistic Regression, SVM, Neural Network解決非線性分類問題
（1）Logistic Regression
Creating some polynomial features based on the original features to map the original examples into a high-dimensional feature space (like the polynomial kernel in SVM)，such as quadratic, cubic features, to solve the non-linear classification problems.
（2）Neural Network
Theoretically, Neural Network with nonlinear activation function is as a non-linear classifier to be able to deal with the nonlinear classification problems.
The Neural Network is same as logistic regression when the number of hidden layer is 0 and the activation function in output layer is sigmoid function.
（3） Support Vector Machine
SVM introduces kernel function to dispose of the non-linear classification problems. Kernel function is able to map the original examples into a high-dimensional feature space so as to solve it by linear classifiers.
Though SVM with linear or polynomial functions is similar to logistic regression, The differences between logistic regression and SVM include:
-The hypothesis is different
-the optimization objective is different
Maybe, the optimization method could be same.