一、support vector machine(large margin classifier)

得到一个参数theta,然后预测y是0还是1：

即最终消去加号左边的项，得到:

二、Kernels

If we want to find non-lineardecision boundary



例:



结果如图所示:

三、SVM with Kernels

四、SVM parameters

Large C: Lower bias, high variance
Small C: Higher bias,low variance

五、Logistic Regression & SVMs

n=number of features, m=number of training examples.

1. if n is large(relative to m), (n>>m, n=10,000, m=10——1000):
   Use logistic regression,or SVM without a kernel(linear kernel).

2. if n is small, m is intermediate(n=1——1000, m=10——10,000)
   Use SVM with Gaussion Kernel.

3. if n is small, m is large(n=1——1000, m=50,000+):
   Creat/Add more features, then use logistic regression or SVM without a kernel.

4. Neural network likely to work well for most of these settings, but may be slower to train.