On the Optimization of Margin Distribution
Authors: Meng-Zhang Qian, Zheng Ai, Teng Zhang, Wei Gao
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this work, we take one step on this direction by providing a new generalization error bound, which is heavily relevant to margin distribution by incorporating ingredients such as average margin and semi-variance, a new margin statistics for the characterization of margin distribution. Inspired by the theoretical findings, we propose the MSVMAv, an efficient approach to achieve better performance by optimizing margin distribution in terms of its empirical average margin and semi-variance. We finally conduct extensive experiments to show the superiority of the proposed MSVMAv approach. |
| Researcher Affiliation | Academia | 1National Key Laboratory for Novel Software Technology, Nanjing University, China 2School of Computer Science and Technology, Huazhong University of Science and Technology, China |
| Pseudocode | Yes | Algorithm 1 The MSVMAv Approach |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We consider 30 datasets, including 20 regular and 10 large-scale datasets. The number of instances varies from 208 to 88588 while the feature dimensionality ranges from 2 to 1836, covering a wide range of properties. The statistics for all datasets can be found in [Qian et al., 2022]. |
| Dataset Splits | Yes | All algorithms are evaluated by 30 times of random partitions of datasets with 80% and 20% of data for training and testing, respectively. ... For our MSVMAv approach, parameters αk and βk are set to be constant and selected by 5-fold cross validation from {2 10, 2 8, , 210} |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | For each dataset, we scale all features into the interval [0, 1], and augment each instance x with constant 1 for the bias of linear model. ... For our MSVMAv approach, parameters αk and βk are set to be constant and selected by 5-fold cross validation from {2 10, 2 8, , 210}, and the width of Gaussian kernel is chosen from {2 10/d, 2 8/d, , 210/d}. We select the maximum iteration number T = 100 as a stopping criteria for MSVMAv . |