SUPPORT VECTOR MACHINE ( SVM)akjhgaskjdgjksdgajkgdagdaakg[1].pptx

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1. SUPPORT VECTOR MACHINE ( SVM) PRESENTED BY MOUMA PRAMANIK

2. INTRODUCTION SVM is a powerful supervised algorithm that works best on smaller datasets but on complex ones. SupportVector Machine, abbreviated as SVM can be used for both regression and classification tasks, butgenerally, they work best in classification problems. They were very famous around the time they were created, during the 1990s, and keep on being the go-to method for a high-performing algorithm with a little tuning.

3. WHY SVM USED SVMs are effective in cases where the number of dimensions (features) is greater than the number of samples. SVMs are memory efficient because they use a subset of the training data in the decision function (called support vectors) . SVMs can handle both linear and non-linear data .

4. TYPES OF SVM Support vector machines are broadly classified into two types: 1) simple or linear SVM 2)kernel or non-linear SVM

5. simple or linear SVM When we can easily separate data with hyperplane by drawing a straight line is Linear SVM

6. kernel or non-linear SVM When we cannot separate data with a straight line we use Non – Linear SVM.

7. ADVANTAGE OF SVM  SVMs are particularly effective in high-dimensional spaces, which means they perform well when the number of features is large relative to the number of samples , SVMs are memory efficient because they only use a subset of the training points in the decision function (support vectors), which reduces the complexity of the model. SVMs can handle both linear and non-linear classification problems by using different kernel functions (e.g., linear, polynomial, radial basis function (RBF), sigmoid).

8. DISADVANTAGE SVMs can be computationally intensive, especially when dealing with large datasets. SVMs do not scale well with large datasets because the training time can become excessively long.

9. CONCLUSION SVMs face challenges when dealing with big data, particularly in the case of nonlinear SVMs . The complexity of nonlinear SVM solvers and the increase in memory requirements.

10. THANK YOU