Data Sparseness in Linear SVM
Authors: Xiang Li, Huaimin Wang, Bin Gu, Charles X. Ling
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We first study the convergence behavior of linear SVM experimentally, and make several observations, useful for real-world applications. We then offer theoretical proofs for our observations by studying the Bayes risk and PAC bound. |
| Researcher Affiliation | Academia | Computer Science Department, University of Western Ontario, Canada School of Computer, National University of Defense Technology, China Nanjing University of Information Science and Technology, China |
| Pseudocode | Yes | Algorithm 1 Data Sampling |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It mentions using Lib-linear but does not offer its own implementation code. |
| Open Datasets | Yes | We use two movie rating datasets: the movielens 1M dataset1 (ml-1m) and the Yahoo Movies dataset2 (ymovie).Footnotes: 1http://grouplens.org/datasets/movielens/ 2http://webscope.sandbox.yahoo.com/catalog.php?datatype=r |
| Dataset Splits | No | The paper discusses training and testing errors and sizes but does not explicitly provide information on a validation dataset split or methodology for hyperparameter tuning. |
| Hardware Specification | No | The paper mentions running experiments on a "modern Xeon CPU" and "on a cluster", but does not provide specific model numbers or detailed hardware specifications. |
| Software Dependencies | No | The paper mentions using "Lib-linear [Fan et al., 2008] with default parameters" but does not provide a specific version number for this or any other software dependency. |
| Experiment Setup | Yes | We use Lib-linear [Fan et al., 2008] with default parameters to train the classifier |