Scalability

Oracle's SVM uses stratified sampling and incremental model building for large data sets.

When dealing with very large data sets, sampling is often required. However, sampling is not required with Oracle Machine Learning for SQL SVM, because the algorithm itself uses stratified sampling to reduce the size of the training data as needed.

Oracle's SVM is highly optimized. It builds a model incrementally by optimizing small working sets toward a global solution. The model is trained until convergence on the current working set, then the model adapts to the new data. The process continues iteratively until the convergence conditions are met. The Gaussian kernel uses caching techniques to manage the working sets.