"In Kernel trick method, We do not need the coordinates of the data in the feature space" This statement is True or False?
Answers
Thank you for asking this question, here is your answer:
No, we do not. The prerequisite to apply the kernel trick is that wherever the algorithm uses a data point x, it is used in an expression of the form xTz, where z is some other point.
The corresponding prediction function, once α’s are known, is given by:
y=f(x)=∑iαiyixTix
If there is any confusion please leave a comment below.
Consequently, you can outline guides x toward ϕ(x) where you don't have to know ϕ(x) unequivocally.
For whatever length of time that you can figure ϕ(x)Tϕ(z), you can supplant all events of xTz by ϕ(x)Tϕ(z) and certainly work in the higher-dimensional space.
Then again to assess hyperplane the SVM needs just help vectors, and other information are a bit much with the exception of those, which are misclassified in delicate edge SVM.