COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Statement 1 is True and 2 is False
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Statement 1 is False and 2 is True
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Both statements are False
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Both Statements are True
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Detailed explanation-1: -19) We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization? Statements one and two are correct. Suppose you are dealing with 4 class classification problem and you want to train a SVM model on the data for that you are using One-vs-all method.
Detailed explanation-2: -The maximum value of a Gaussian kernel is 1. Suppose we have a data set with $n=10$ features and $m=5000$ examples. set and does not achieve the desired performance on the training or cross-validation sets.
Detailed explanation-3: -What is the purpose of the Kernel Trick? To transform the problem from supervised to unsupervised learning.
Detailed explanation-4: -SVM Kernel Functions The function of kernel is to take data as input and transform it into the required form. Different SVM algorithms use different types of kernel functions. These functions can be different types. For example linear, nonlinear, polynomial, radial basis function (RBF), and sigmoid.