Improving Efficiency of SVMk-Fold Cross-Validation by Alpha Seeding
Authors: Zeyi Wen, Bin Li, Ramamohanarao Kotagiri, Jian Chen, Yawen Chen, Rui Zhang
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results show that our algorithms are several times faster than the k-fold cross-validation which does not make use of the previously trained SVM. Moreover, our algorithms produce the same results (hence same accuracy) as the k-fold cross-validation which does not make use of the previously trained SVM. |
| Researcher Affiliation | Academia | 1{zeyi.wen, kotagiri, rui.zhang}@unimelb.edu.au The University of Melbourne, Australia 2{gitlinux@gmail.com, ellachen@scut.edu.cn, elfairyhyuk@gmail.com} South China University of Technology, China |
| Pseudocode | No | The pseudo-code of the full algorithm is shown in Algorithm 1 in Wen et al. (2016). |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code related to the methodology described. |
| Open Datasets | Yes | We empirically evaluate our proposed algorithms using five datasets from the Lib SVM website (Chang and Lin 2011). |
| Dataset Splits | Yes | The k-fold cross-validation is a commonly used process to evaluate the effectiveness of SVMs with the selected hyper-parameters. We varied k from 3 to 100 to study the effect of the value of k. |
| Hardware Specification | Yes | The experiments were conducted on a desktop computer running Linux with a 6-core E5-2620 CPU and 128GB main memory. |
| Software Dependencies | No | All our proposed algorithms were implemented in C++. We empirically evaluate our proposed algorithms using five datasets from the Lib SVM website (Chang and Lin 2011). The paper mentions software used (C++, Lib SVM) but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | Following the common settings, we used the Gaussian kernel function and by default k is set to 10. The hyper-parameters for each dataset are identical to the existing studies (Catanzaro, Sundaram, and Keutzer 2008; Smirnov, Sprinkhuizen-Kuyper, and Nalbantov 2004; Wu and Li 2006). Table 2 gives more details about the datasets. |