SVM和logistics笔记

SVM的参数选择：

C（1/lambda）:Large C： lower bias，high variance

small C，Higher bias， low variance

sigma^2: Large sigma^2: Feature fi, vary more smoothly

Higher bias, lower variance

small sigma^2: Feature fi vary loss less smoothly

Lower bias, higher variance

SVM 和Logistics回归的比较

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

if  n is large(relative to m): e.g. n>=m, n=10000, m=10...1000

Use logistic regression, or SVM without a kernel("linear kernel")

if n is small, m is intermediate (n=1000, m=10-10000)

Use SVM with Gaussian kernel

if n is small, m is large

Create/add more features, then use logistic regression or SVM without a kernel

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