SVM has become one of the state-of-the-art machine learning models for many tasks with excellent results in many practical applications. One of the greatest advantages of SVM is that they are very effective when working on high-dimensional spaces, that is, on problems which have a lot of features to learn from. They are also very effective when the data is sparse (think about a high-dimensional space with very few instances). Besides, they are very efficient in terms of memory storage, since only a subset of the points in the learning space is used to represent the decision surfaces.
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