Research map / Optimization / Nonconvex and smooth optimization
Veto-Consensus Multiple Kernel Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace CCCP/AM/SGD with the PDDP global optimizer for training | VCMKL (linear kernels) · UCI pima | testing accuracy (%): 71.38 -> 78.91 | Helped |
| use PDDP instead of CCCP | VCMKL (linear kernels) · UCI pima | objective (1e-8): 8.784 -> 6.247 | Helped |
| use PDDP instead of SGD | VCMKL (linear kernels) · UCI pima | elapsed time (s): 34 -> 417 | Hurt |
| use RBF-kernel VCMKL instead of best competing method | VCMKL · Vowel | testing cost (c2:c1=1:1): 0.111 -> 0.025 | Helped |
| use linear kernels only in VCMKL vs other methods | l-VCML · Robot, Music, Vetebral, Vowel | testing cost | No clear effect |
| increase false positive cost (c2:c1 from 1:1 to 1:4) setting | rbf-VCML · Robot, Music, Vetebral, Vowel | testing cost rank: rank 1 on all four sets at 1:4 | Helped |
| increase number of kernels M beyond number of hidden subgroups (ablation) | VCMKL · Robot and Vowel | testing accuracy | Hurt |
| increase number of kernels M from 1 up to number of subgroups (ablation) | VCMKL · Robot and Vowel | testing accuracy | Helped |
Closest papers by meaning
- Joker: Joint Optimization Framework for Lightweight Kernel Machines (ICML 2025 · similarity 0.86)
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