COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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1 is True but 2 is False
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1 is False but 2 is True
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Both are True
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Both are False
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Detailed explanation-1: -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.
Detailed explanation-2: -c. They find “maximum margin” classifiers. It’s a true statement that Support Vector Machines (SVMs) find “maximum margin” classifiers. SVMs are used and helpful when one wants the classifier to have the largest margin possible because they maximize the margin.
Detailed explanation-3: -Similarity features with Gaussian RBF kernel. Another method to add more features to the data is to use the so-called similarity features. A similarity feature measures how far a value of an existing feature is from a landmark.
Detailed explanation-4: -Kernels are used in Support Vector Machines (SVMs) to solve regression and classification problems. Support Vector Machines use the Kernel Trick to transform linearly inseparable data into linearly separable data, thus finding an optimal boundary for possible outputs.