APPLICATION OF SUPERVISED LEARNING
SUPPORT VECTOR MACHINE SVM
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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No
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Yes
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Either A or B
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None of the above
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Detailed explanation-1: -SVMs are well known for their effectiveness in high dimensional spaces, where the number of features is greater than the number of observations. The model complexity is of O(n-features * n² samples) so it’s perfect for working with data where the number of features is bigger than the number of samples.
Detailed explanation-2: -The use of kernel helps in transforming the input data into high-dimensional space which enables the SVM algorithm to solve non-linear classification problems.
Detailed explanation-3: -Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is highly dependent on the size of data set.