Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms
Authors: Yunwen Lei, Urun Dogan, Alexander Binder, Marius Kloft
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | 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 yunwelei@cityu.edu.hk Ur un Dogan Microsoft Research Cambridge CB1 2FB, UK udogan@microsoft.com Alexander Binder ISTD Pillar Singapore University of Technology and Design Machine Learning Group, TU Berlin alexander binder@sutd.edu.sg Marius Kloft Department of Computer Science Humboldt University of Berlin kloft@hu-berlin.de |
| 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. |