This is versatile enough to adapt to the linear separability of data. For a separable dataset, SVM, with linear kernel, performs comparably to logistic regression. Beyond this, SVM also works well for a non-separable one, if equipped with a non-linear kernel, such as RBF. For a high-dimensional dataset, the performance of logistic regression is usually compromised, while SVM still performs well. A good example of this can be in news classification, where the feature dimensionality is in the tens of thousands. In general, very high accuracy can be achieved by SVM with the right kernel and parameters. However, this might be at the expense of intense computation and high memory consumption.

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