Multi-Class Optimal Margin Distribution Machine
Authors: Teng Zhang, Zhi-Hua Zhou
ICML 2017 | Conference PDF | Archive PDF | Plain Text | 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 λ 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 λ 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 µ and θ are both selected from [0.2, 0.4, 0.6, 0.8]. |