Scaling SVM and Least Absolute Deviations via Exact Data Reduction
Authors: Jie Wang, Peter Wonka, Jieping Ye
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have evaluated DVI on both synthetic and real data sets. Experiments indicate that DVI significantly outperforms the existing state-of-the-art screening rules for SVM, and it is very effective in discarding non-support vectors for LAD. |
| Researcher Affiliation | Academia | Jie Wang JIE.WANG.USTC@ASU.EDU Arizona State University, Tempe, AZ 85287 USA Peter Wonka PWONKA@GMAIL.COM King Abdullah University of Science and Technology, Thuwal, Saudi Arabia Arizona State University, Tempe, AZ 85287 USA Jieping Ye JIEPING.YE@ASU.EDU Arizona State University, Tempe, AZ 85287 USA |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the code for the work described in this paper, nor does it provide a direct link to a source-code repository. |
| Open Datasets | Yes | We compare the performance of DVIs, SSNSV and ESSNSV using (a) the IJCNN1 data set (Prokhorov, 2001); (b) the Wine Quality data set (Cortez et al., 2009); and (c) the Forest Covertype data set (Hettich & Bay, 1999). We evaluate the performance of DVIs for LAD on three real data sets: (a) Magic Gamma Telescope data set (Bache & Lichman, 2013); (b) Computer data set (Rasmussen et al.); (c) Houses data set (Pace & Barry, 1997). |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We test the rules along a sequence of 100 parameters of C [10^-2, 10] equally spaced in the logarithmic scale. |