3 papers
cs.LG2018
Dual SVM Training on a Budget
Sahar Qaadan, Merlin Schüler, Tobias Glasmachers
We present a dual subspace ascent algorithm for support vector machine training that respects a budget constraint limiting the number of support vectors. Budget methods are effecti…
cs.LG2018
Speeding Up Budgeted Stochastic Gradient Descent SVM Training with Precomputed Golden Section Search
Tobias Glasmachers, Sahar Qaadan
Limiting the model size of a kernel support vector machine to a pre-defined budget is a well-established technique that allows to scale SVM learning and prediction to large-scale d…
cs.LG2018
Multi-Merge Budget Maintenance for Stochastic Gradient Descent SVM Training
Sahar Qaadan, Tobias Glasmachers
Budgeted Stochastic Gradient Descent (BSGD) is a state-of-the-art technique for training large-scale kernelized support vector machines. The budget constraint is maintained increme…