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What are the characteristics of SVM?
A classification approach that has received considerable scrutiny is the support vector machine (SVM). This approach has its roots in statistical learning theory and has displayed promising empirical outcomes in several practical applications, from handwritten digit identification to text classification.
SVM also operates with high-dimensional data and prevents the curse of dimensionality issues. There is the second element of this approach is that it defines the decision boundary using a subset of the training instances, called the support vectors.
SVM can be prepared to explicitly view this type of hyperplane in linearly separable data. It can achieve by displaying how the SVM methodology can be continued to non-linearly separable data. The data set is linearly separable; i.e., it can discover a hyperplane including all the squares residing on one side of the hyperplane and all the circles residing on the different sides.
The capacity of a linear model is inversely associated with its margin. Models with small margins have larger capacities because they are dynamic and can fit some training sets, unlike models with high margins. As per the SRM principle, as the capacity enhances, the generalization error bound can increase. Hence, it is desirable to make linear classifiers that maximize the margins of their decision boundaries to provide that their worst-case generalization errors are reduced.
A linear SVM is a classifier that checks for a hyperplane with the highest margin, which is called a maximal margin classifier. It can learn how SVM learns such a boundary, it can start with some preliminary analysis about the decision boundary and margin of a linear classifier.
There are various characteristics of SVM which are as follows −
The SVM learning problem can be organized as a convex optimization issue, in which effective algorithms are accessible to discover the global minimum of the objective function. There are different classification methods, including rule-based classifiers and artificial neural networks that employ a greedy-based approach to search the hypothesis area. Such methods influence finding only locally optimum solutions.
SVM implements capacity control by enlarging the margin of the decision boundary. The user should provide several parameters including the type of kernel function to use and the cost function C for offering each slack variable.
SVM can be used to categorical record by learning dummy variables for each categorical attribute value shown in the data. For instance, if the marital status has three values such as single, married, divorced and it can learn a binary variable for each of the attribute values.
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