We present an optimization approach for linear SVMs based on a stochastic primal-dual approach, where the primal step is akin to an importance-weighted SGD, and the dual step is a stochastic update on the importance weights. This yields an optimization method with a sublinear dependence on the training set size, and the first method for learning linear SVMs with runtime less then the size of the training set required for learning!

We present an optimization approach for linear SVMs based on a stochastic primal-dual approach, where the primal step is akin to an importance-weighted SGD, and the dual step is a stochastic update on the importance weights. This yields an optimization method with a sublinear dependence on the training set size, and the first method for learning linear SVMs with runtime less then the size of the training set required for learning! Papers published at the Neural Information Processing Systems Conference.

After the Statsbot team published the post about time series anomaly detection, many readers asked us to tell them about the Support Vector Machines approach. It's time to catch up and introduce you to SVM without hard math and share useful libraries and resources to get you started. If you have used machine learning to perform classification, you might have heard about Support Vector Machines (SVM). Introduced a little more than 50 years ago, they have evolved over time and have also been adapted to various other problems like regression, outlier analysis, and ranking. SVMs are a favorite tool in the arsenal of many machine learning practitioners.

We present an optimization approach for linear SVMs based on a stochastic primal-dual approach, where the primal step is akin to an importance-weighted SGD, and the dual step is a stochastic update on the importance weights. This yields an optimization method with a sublinear dependence on the training set size, and the first method for learning linear SVMs with runtime less then the size of the training set required for learning!