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Which kernel function is commonly used in SVM for non-linear classification?
Which kernel function is commonly used in SVM for non-linear classification?
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Which kernel function is commonly used in SVM for non-linear classification?
Linear kernel
Polynomial kernel
Radial basis function (RBF) kernel
Sigmoid kernel
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The Radial Basis Function (RBF) kernel is popular for handling non-linear classification problems.
Questions & Step-by-step Solutions
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Q: Which kernel function is commonly used in SVM for non-linear classification?
Solution:
The Radial Basis Function (RBF) kernel is popular for handling non-linear classification problems.
Steps: 5
Show Steps
Step 1: Understand what SVM (Support Vector Machine) is. It is a type of machine learning algorithm used for classification tasks.
Step 2: Know that SVM can work with both linear and non-linear data.
Step 3: Realize that for non-linear classification, we need a special function called a 'kernel' to help SVM separate the data.
Step 4: Learn that the Radial Basis Function (RBF) kernel is a commonly used kernel for non-linear classification.
Step 5: Remember that the RBF kernel helps to transform the data into a higher dimension where it can be separated more easily.
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