Multi-Merge Budget Maintenance for Stochastic Gradient Descent SVM Training
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 incrementally by merging two points whenever the pre-defined budget is exceeded. The process of finding suitable merge partners is costly; it can account for up to 45% of the total training time. In this paper we investigate computationally more efficient schemes that merge more than two points at once. We obtain significant speed-ups without sacrificing accuracy.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Speeding Up Budgeted Stochastic Gradient Descent SVM Training with Precomputed Golden Section Search
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 data. Its core addition to simple stochas…
Stochastic Wasserstein Gradient Flows using Streaming Data with an Application in Predictive Maintenance
We study estimation problems in safety-critical applications with streaming data. Since estimation problems can be posed as optimization problems in the probability space, we devise a stochastic projected Wasserstein gra…
Decision MakingBridge-Centered Metapath Classification Using R-GCN-VGAE for Disaster-Resilient Maintenance Decisions
Daily infrastructure management in preparation for disasters is critical for urban resilience. When bridges remain resilient against disaster-induced external forces, access to hospitals, shops, and residences via metapa…
Welfare Maximization Algorithm for Solving Budget-Constrained Multi-Component POMDPs
Partially Observable Markov Decision Processes (POMDPs) provide an efficient way to model real-world sequential decision making processes. Motivated by the problem of maintenance and inspection of a group of infrastructu…
Decision MakingSequential Decision MakingOn the Role of Batch Size in Stochastic Conditional Gradient Methods
We study the role of batch size in stochastic conditional gradient methods under a $μ$-Kurdyka-Łojasiewicz ($μ$-KL) condition. Focusing on momentum-based stochastic conditional gradient algorithms (e.g., Scion), we deriv…