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..
Ramp Loss Linear Programming Support Vector Machine
Authors: Xiaolin Huang, Lei Shi, Johan A.K. Suykens
JMLR 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the numerical experiments, we evaluate the performance of ramp-LPSVM (6) and its problem-solving algorithms. We first report the optimization performance and then discuss the robustness and the sparsity compared with C-SVM, LPSVM (5), and ramp-SVM (11). |
| Researcher Affiliation | Academia | Xiaolin Huang EMAIL Department of Electrical Engineering, ESAT-STADIUS, KU Leuven Kasteelpark Arenberg 10, Leuven, B-3001, Belgium Lei Shi EMAIL Department of Electrical Engineering, ESAT-STADIUS, KU Leuven School of Mathematical Sciences, Fudan University, Shanghai, 200433, P.R. China Johan A.K. Suykens EMAIL Department of Electrical Engineering, ESAT-STADIUS, KU Leuven Kasteelpark Arenberg 10, Leuven, B-3001, Belgium |
| Pseudocode | Yes | Algorithm 1: DC programming for ramp-LPSVM from ˆα,ˆb [...] Algorithm 2: Global Search for ramp-LPSVM |
| Open Source Code | No | The paper mentions using 'Matlab R2011a' and a 'Genetic Algorithm (GA) toolbox' developed by Chipperfield et al. (1994) for experiments, but it does not provide any statement or link to open-source code for their own proposed methodology (ramp-LPSVM). |
| Open Datasets | Yes | The data are downloaded from the UCI Machine Learning Repository given by Frank and Asuncion (2010). |
| Dataset Splits | Yes | In data sets Spect , Monk1 , Monk2 , and Monk3 , the training and the testing sets are provided. For the others, we randomly partition the data into two parts: half data are used for training and the remaining data are for testing. |
| Hardware Specification | Yes | The experiments are done in Matlab R2011a in Core 2-2.83 GHz, 2.96G RAM. |
| Software Dependencies | Yes | The experiments are done in Matlab R2011a |
| Experiment Setup | Yes | In our experiments, we apply a Gaussian kernel K(xi, xj) = exp xi xj 2/σ2 . The training data are normalized to [0, 1]n and then the regularization coefficient µ and the kernel parameter σ are tuned by 10-fold cross-validation for each method. In the tuning phase, grid search using logarithmic scale is applied. The range of possible µ value is [10 2, 103] and the range of σ value is between 10 3 and 102. For ramp-LPSVM, since the global search needs more computation time, the parameters tuning by cross-validation is conducted based on Algorithm 1. [...] Set δ (the threshold of convergence for DC programming), ε (the difference value in hill detouring), Kstep (the maximal number of hill detouring steps). |