What are the limitations of SVM in large datasets?

SVM becomes slow and memory-intensive with large datasets due to quadratic optimization complexity.

SVM training involves solving a quadratic programming problem that scales poorly with dataset size. Kernel SVMs are especially expensive because they compute pairwise similarities, leading to O(n²) memory and time complexity.