COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Flexibility in choosing a similarity function
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Sparseness of solution when dealing with large data sets
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Answer A
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Both A & B
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Detailed explanation-1: -Their “fame” mainly rests on two key properties: 1) SVMs find solutions of classification problems that have “generalization in mind", and 2) they are able to find non-linear solutions efficiently using the “kernel trick".
Detailed explanation-2: -The objective of SVM is to maximise the width of the separation gap. That means to maximise 2/||W|| which is same as minimising ||W|| which is same as minimising ||W||² and which is same as minimising (1/2)||W||² and the same thing can be written as (1/2)WᵗW. And this is done for mathematical convenience.
Detailed explanation-3: -Support Vector Machine(SVM) is a supervised machine learning algorithm used for both classification and regression.
Detailed explanation-4: -SVM works by mapping data to a high-dimensional feature space so that data points can be categorized, even when the data are not otherwise linearly separable. A separator between the categories is found, then the data are transformed in such a way that the separator could be drawn as a hyperplane.