Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms
Authors: Yunwen Lei, Urun Dogan, Alexander Binder, Marius Kloft
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Benchmarks on several real-world datasets show that the proposed algorithm can achieve significant accuracy gains over the state of the art. |
| Researcher Affiliation | Collaboration | Yunwen Lei Department of Mathematics City University of Hong Kong EMAIL Ur un Dogan Microsoft Research Cambridge CB1 2FB, UK EMAIL Alexander Binder ISTD Pillar Singapore University of Technology and Design Machine Learning Group, TU Berlin alexander EMAIL Marius Kloft Department of Computer Science Humboldt University of Berlin EMAIL |
| Pseudocode | Yes | Algorithm 1: Training algorithm for ℓp-norm MC-SVM. |
| Open Source Code | No | The paper states 'We implemented the proposed ℓp-norm MC-SVM algorithm (Algorithm 1) in C++' but does not provide any link or explicit statement about releasing the source code. |
| Open Datasets | Yes | We experiment on six benchmark datasets: the Sector dataset studied in [26], the News 20 dataset collected by [27], the Rcv1 dataset collected by [28], the Birds 15, Birds 50 as a part from [29] and the Caltech 256 collected by griffin2007caltech. |
| Dataset Splits | Yes | We employ a 5-fold cross validation on the training set to tune the regularization parameter C by grid search over the set {2 12, 2 11, . . . , 212} and p from 1.1 to 2 with 10 equidistant points. |
| Hardware Specification | No | The paper does not specify any hardware components (e.g., CPU, GPU models) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'implemented the proposed ℓp-norm MC-SVM algorithm (Algorithm 1) in C++' but does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | We employ a 5-fold cross validation on the training set to tune the regularization parameter C by grid search over the set {2 12, 2 11, . . . , 212} and p from 1.1 to 2 with 10 equidistant points. |