Why Svm Outperforms Other Classifiers?

There are many algorithms used for classification in machine learning but SVM is better than most of the other algorithms used as it has a better accuracy in results. ... classification, Support Vector Machine Algorithm has a faster prediction along with better accuracy.

How is SVM different from other classifiers?

SVM is a very efficient & simple classifier algorithm which is widely used for pattern recognition. Also it can have a very good classification performance than any other classifier. ... In SVM approach, the main aim of an SVM classifier is obtaining a function f(x), which determines the decision boundary or hyper plane.

Why is SVM the best classifier?

Advantages. SVM Classifiers offer good accuracy and perform faster prediction compared to Naïve Bayes algorithm. They also use less memory because they use a subset of training points in the decision phase. SVM works well with a clear margin of separation and with high dimensional space.