A Minimax Approach to Supervised Learning
Authors: Farzan Farnia, David Tse
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform several numerical experiments to show the power of the minimax SVM in outperforming the SVM. |
| Researcher Affiliation | Academia | Farzan Farnia farnia@stanford.edu David Tse dntse@stanford.edu Department of Electrical Engineering, Stanford University, Stanford, CA 94305. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We evaluated the performance of the minimax SVM on six binary classification datasets from the UCI repository |
| Dataset Splits | No | We determined the parameters by cross validation, where we used a randomly-selected 70% of the training set for training and the rest 30% for testing. ... We performed this procedure in 1000 Monte Carlo runs each training on 70% of the data points and testing on the rest 30% and averaged the results. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Matlab s SVM command' but does not provide specific version numbers for Matlab or any other software dependencies. |
| Experiment Setup | Yes | We implemented the minimax SVM by applying the subgradient descent to (18) with the regularizer λ α 2 2. We determined the parameters by cross validation, where we used a randomly-selected 70% of the training set for training and the rest 30% for testing. We tested the values in {2 10, . . . , 210}. |