- $X$ is a
ncolumns, that means it is a $m \times n$ matrix, represents for training set.
- $\theta$ is a $1 \times n$
vector, stands for hypothesis parameter.
- $y$ is a $m \times 1$
vector, stands for real value of training set.
- $\alpha$ named
learning ratefor defining learning or descending speed.
Draw hypothesis of a pattern.
Since classification problem range from 0 to 1
We need to make use of this
Calculate the Cost for single training point.
3. Cost function
Draw cost function for iterating whole training set.
4. Get optimized parameter
Learn from training set to get optimized parameter for proposed algorithm.
- Conjugate gradient