Using SVM software package to solve for parameters θ
Need to specify:
- Choice of parameter C.
- Choice of kenel (similarity function):
- No kernel (“linear kernel”)
Predict "y = 1" if θ ^T * x ≥ 0 θ0 + θ1x1 + ... θnxn ≥ 0, ---> n large, m small
- Gaussian kernel:
DO perform feature scaling before using the Gaussian kernel
Other choices of kernel
Logistic regression VS. SVMs
n = number of features m = number of training examples
nis large (relative to
- Use logistic regeression, or SVM without a kernel (“linear kernel”)
- Use SVM with Gaussian kernel**
- Create / add more feature, then use logistic regression or SVM without a kernel
- Neural nerwork likely to work well for most of these settings, but may be slower to train.