Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Data Sparseness in Linear SVM
Authors: Xiang Li, Huaimin Wang, Bin Gu, Charles X. Ling
IJCAI 2015 | Venue PDF | 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 |