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 and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Multi-Class Optimal Margin Distribution Machine
Authors: Teng Zhang, Zhi-Hua Zhou
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical study further shows that mc ODM always outperforms all four versions of multi-class SVMs on all experimental data sets. |
| Researcher Affiliation | Academia | National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China. |
| Pseudocode | Yes | Algorithm 1 Kenrel mc ODM ... Algorithm 2 Solving the sub-problem |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for their method is openly available. |
| Open Datasets | Yes | Table 1 summarizes the statistics of these data sets. The data set size ranges from 150 to more than 581,012, and the dimensionality ranges from 4 to more than 62,061. Moreover, the number of class ranges from 3 to 1,000, so these data sets cover a broad range of properties. |
| Dataset Splits | Yes | For all the methods, the regularization parameter ฮป for mc ODM or C for binary SVM and mc SVM is selected by 5-fold cross validation from [20, 22, . . . , 220]. |
| Hardware Specification | Yes | All the experiments are performed with MATLAB 2012b on a machine with 8 2.60 GHz CPUs and 32GB main memory. |
| Software Dependencies | Yes | All the experiments are performed with MATLAB 2012b on a machine with 8 2.60 GHz CPUs and 32GB main memory. The binary SVM used in ova SVM, ovo SVM and mc SVM are both implemented by the LIBLINEAR (Fan et al., 2008) package. |
| Experiment Setup | Yes | For all the methods, the regularization parameter ฮป for mc ODM or C for binary SVM and mc SVM is selected by 5-fold cross validation from [20, 22, . . . , 220]. For mc ODM, the regularization parameters ยต and ฮธ are both selected from [0.2, 0.4, 0.6, 0.8]. |