Suppose that we want to build a SVM classifier that classifies two-dimensional data (i.e., X = [x1, x2]) into two classe
Posted: Fri Jul 01, 2022 5:37 am
Suppose that we want to build a SVM classifier that classifiestwo-dimensional data (i.e., X = [x1, x2]) into two classes:diamonds and crosses. We have a set of training data that isplotted as follows
Explain how you would build the SVM classifier with respect tothe classification problem provided by providing detailedexplanations for the followings:
i. The support vector optimization function used to classify thetwo different classes. Is there a need to use a kernel function tosolve the problem? Why? And which kernel is most suitable?Explain.
ii. The concept of large margin classifier with regards to SVMand the problem presented.
iii. What is the C parameter in SVM, and how will it affect thefinal classification result for the given problem?
X2 800 IX
- Suppose That We Want To Build A Svm Classifier That Classifies Two Dimensional Data I E X X1 X2 Into Two Classe 1 (43.42 KiB) Viewed 35 times
i. The support vector optimization function used to classify thetwo different classes. Is there a need to use a kernel function tosolve the problem? Why? And which kernel is most suitable?Explain.
ii. The concept of large margin classifier with regards to SVMand the problem presented.
iii. What is the C parameter in SVM, and how will it affect thefinal classification result for the given problem?
X2 800 IX