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When using SVMs we can apply an almost miraculous mathematical technique for adding polynomial features and similarity features called the
Kernel trick
Shell trick
Mapping and Reducing
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1 Support Vector Machines Part-1
2 A Support Vector Machine can be used for...
3 Support Vector Machines Part-2
4 The decision boundaries in a Support Vector machine is fully...
5 Support Vector Machines are not sensitive to feature scaling...
6 If we strictly impose that all instances be off the...
7 The main issues with hard margin classification are...
8 The objectives of Soft Margin Classification are to find a...
9 The balance between keeping the street as large as possible...
10 A smaller C value leads to a wider street but...
11 If your SVM model is overfitting, you can try regularizing...
12 Problems with adding polynomial features are...
13 The hyperparameter coef0 of SVC controls how much the model...
14 A similarity function like Gaussian Radial Basis Function is used...
15 When adding features with similarity function, and creating a landmark...
16 When using SVMs we can apply an almost miraculous mathematical...
17 Which is right for the gamma parameter of SVC which...
18 LinearSVC is much faster than SVC(kernel="linear"))...
19 In SVM regression the model tries to...
20 The SVR class is the regression equivalent of the SVC...
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