Classification with Minimax Distance Measures
Authors: Morteza Haghir Chehreghani
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally study our framework on different synthetic and real-world datasets and illustrate its effectiveness and superior performance in different settings. |
| Researcher Affiliation | Industry | Morteza Haghir Chehreghani Xerox Research Centre Europe XRCE 6 chemin de Maupertuis 38240 Meylan, France morteza.chehreghani@xrce.xerox.com |
| Pseudocode | No | The paper describes algorithms and procedures in prose but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository links or explicit statements of code release) for the source code of the described methodology. |
| Open Datasets | Yes | We perform our real-world experiments on twelve datasets from different domains, selected from the UCI repository (Lichman 2013). |
| Dataset Splits | No | The paper mentions that '60% of the objects are used for training' but does not specify a separate validation split or explicit cross-validation details for hyperparameter tuning. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using Logistic Regression and SVM but does not provide specific version numbers for these software libraries or any other dependencies. |
| Experiment Setup | Yes | With SVM, we examine three different kernels: i. linear (lin), ii. radial basis function (rbf), and iii. sigmoid (sig), and choose the best result. With Minimax distances, we only use the linear kernel, since we assume that Minimax distances must be able to capture the correct classes, such that they can be then discriminated via a linear separator. ... We have repeated the random split of the data for 20 times and report the average results. |