D-optimal Coreset
Cross-source consensus on D-optimal Coreset from 1 sources and 5 claims.
1 sources · 5 claims
How it works
Benefits
Risks & contraindications
Highlighted claims
- D-optimal design selects a small informative coreset from the candidate set using non-negative candidate weights. — A PPA-Driven 3D-IC Partitioning Selection Framework with Surrogate Models
- Candidates enter the coreset when their D-optimal weights exceed a threshold. — A PPA-Driven 3D-IC Partitioning Selection Framework with Surrogate Models
- Weighted least squares fits the surrogate by minimizing weighted squared error over coreset candidates. — A PPA-Driven 3D-IC Partitioning Selection Framework with Surrogate Models
- Lower D-optimal thresholds usually enlarge the coreset and improve coverage of true top-10 candidates, but do not guarantee better prediction ranking. — A PPA-Driven 3D-IC Partitioning Selection Framework with Surrogate Models
- D-optimal design is useful because fitting information depends mainly on feature dimension rather than raw candidate count. — A PPA-Driven 3D-IC Partitioning Selection Framework with Surrogate Models