Research map / Learning theory and algorithms / Kernels and regression theory
Fast Algorithms for Linear and Kernel SVM+
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use privileged textual descriptions with linear SVM+ instead of standard linear SVM | Linear SVM · MNIST+ test | accuracy: 81.73 -> 84.62 | Helped |
| use the dual coordinate descent solver instead of gSMO for linear SVM+ | Linear SVM+ · MNIST+ training | training time: 72.1 -> 9.5 | Helped |
| use privileged textual descriptions with linear SVM+ instead of standard linear SVM | Linear SVM · Scene-15 test | accuracy: 77.56 -> 78.10 | Helped |
| use the dual coordinate descent solver instead of gSMO for linear SVM+ | Linear SVM+ · Scene-15 training | training time: 2.52 -> 0.87 | Helped |
| use ℓ2-loss SVM+ with privileged descriptions instead of standard SVM | Kernel SVM · MNIST+ test (Gaussian kernel) | accuracy: 92.34 -> 93.15 | Helped |
| reformulate kernel SVM+ as a one-class SVM problem and solve it with the LIBSVM SMO implementation | Kernel SVM+ · MNIST+ training (Gaussian kernel) | training time: 54.9 -> 1.3 | Helped |
| use ℓ2-loss SVM+ with privileged features instead of standard SVM | Kernel SVM · Scene-15 test (Gaussian kernel) | accuracy: 78.79 -> 80.57 | Helped |
| reformulate kernel SVM+ as a one-class SVM problem and use LIBSVM | Kernel SVM+ · Scene-15 training (Gaussian kernel) | training time: 12.07 -> 1.08 | Helped |
| use privileged textual tags with kernel SVM+ instead of standard SVM | Kernel SVM · NUS-WIDE test | MAP: 54.41 -> 55.68 | Helped |
| use the ℓ2-SVM+ solver instead of MAT-SVM+ within mi-SVM-PI | mi-SVM-PI · NUS-WIDE training | training time: 765.47 -> 24.26 | Helped |
Closest papers by meaning
- Faster Algorithms for Structured Linear and Kernel Support Vector Machines (ICLR 2025 · similarity 0.86)
- Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction (ICML 2017 · similarity 0.85)
- Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales (NeurIPS 2022 · similarity 0.85)
- Fast and Accurate Refined Nyström-Based Kernel SVM (AAAI 2016 · similarity 0.85)
- Multi-View Randomized Kernel Classification via Nonconvex Optimization (AAAI 2024 · similarity 0.83)
- Joker: Joint Optimization Framework for Lightweight Kernel Machines (ICML 2025 · similarity 0.83)
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