Multi-Class Support Vector Machine via Maximizing Multi-Class Margins
Authors: Jie Xu, Xianglong Liu, Zhouyuan Huo, Cheng Deng, Feiping Nie, Heng Huang
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiment, it shows that our model can get better or compared results when comparing with other related methods. ... Experiments on benchmark datasets show that our model can get equal or better results than other related methods. |
| Researcher Affiliation | Academia | 1Xidian University, Xi an 710071, China 2School of Computer Science and Engineering, Beihang University, China 3University of Texas at Arlington, USA 4Northwestern Polytechnical University, China |
| Pseudocode | Yes | Algorithm 1 SVRG to solve problem (25) |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the methodology described. |
| Open Datasets | Yes | Six multi-class classification datasets from UCI machine learning repository are used in our experiment [Lichman, 2013], main information are listed in Table 1. |
| Dataset Splits | Yes | We use 5 times 5-fold cross validation and compute average accuracy for each method as final performance. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., specific libraries, frameworks, or programming language versions). |
| Experiment Setup | Yes | In all experiments, we automatically tune the parameters by selecting among the values {10r, r { 5, ..., 5}}. We select the largest learning rate for each method and ensure that objective function value is decreasing during optimization. |