Exclusivity Regularized Machine: A New Ensemble SVM Classifier
Authors: Xiaojie Guo, Xiaobo Wang, Haibin Ling
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Theoretical analysis on convergence, global optimality and linear complexity of the proposed algorithm, as well as experiments are provided to reveal the efficacy of our method and show its superiority over state-of-the-arts in terms of accuracy and efficiency. |
| Researcher Affiliation | Academia | Xiaojie Guo Xiaobo Wang Haibin Ling State Key Laboratory of Information Security, IIE, Chinese Academy of Sciences University of Chinese Academy of Sciences National Laboratory of Pattern Recognition, IA, Chinese Academy of Sciences Department of Computer and Information Sciences, Temple University xj.max.guo@gmail.com xiaobo.wang@ia.ac.cn hbling@temple.edu |
| Pseudocode | Yes | Algorithm 1: W Solver |
| Open Source Code | No | The paper does not provide any links to its own source code or state that it is open-sourced. It mentions that 'The codes of DRM are downloaded from the authors website' and 'those of Ada Tree and Bag Tree are integrated in the Matlab statistics toolbox (fitensemble)', but this refers to third-party or baseline code, not the code for Ex RM. |
| Open Datasets | Yes | We adopt several popular UCI benchmark datasets for performance evaluation.3 All experiments are conducted on a machine with 2.5 GHz CPU and 64G RAM. ... 3Available at www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets |
| Dataset Splits | Yes | All the results shown in this experiment are averaged over 10 independent trials, each of which randomly samples half data from the sonar dataset for training and the other half for testing. |
| Hardware Specification | Yes | All experiments are conducted on a machine with 2.5 GHz CPU and 64G RAM. |
| Software Dependencies | No | The paper mentions 'Matlab statistics toolbox (fitensemble)' for baselines but does not provide version numbers for any software, including its own implementation. |
| Experiment Setup | Yes | Based on this evaluation, we set λ to 2 for both L1-Ex RM and L2-Ex RM in the rest experiments. ... The cases correspond to p := 1 take more iterations than p := 2 (about 70 iterations), but they are still very efficient. ... We set λ to 2 for both L1-Ex RM and L2-Ex RM in the rest experiments. |