Exploring the Entire Regularization Path for the Asymmetric Cost Linear Support Vector Machine
This work addresses hyperparameter optimization for SVMs, offering incremental improvements in flexibility for machine learning practitioners.
The authors tackled the problem of exploring the full regularization path for asymmetric-cost linear SVMs, enabling greater flexibility in hyperparameter tuning and providing insights into challenges faced by one-dimensional path algorithms, as demonstrated on synthetic and real data.
We propose an algorithm for exploring the entire regularization path of asymmetric-cost linear support vector machines. Empirical evidence suggests the predictive power of support vector machines depends on the regularization parameters of the training algorithms. The algorithms exploring the entire regularization paths have been proposed for single-cost support vector machines thereby providing the complete knowledge on the behavior of the trained model over the hyperparameter space. Considering the problem in two-dimensional hyperparameter space though enables our algorithm to maintain greater flexibility in dealing with special cases and sheds light on problems encountered by algorithms building the paths in one-dimensional spaces. We demonstrate two-dimensional regularization paths for linear support vector machines that we train on synthetic and real data.