Discrete Rényi Classifiers
Authors: Meisam Razaviyayn, Farzan Farnia, David Tse
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we numerically compare our proposed algorithm with the DCC classifier and show that the proposed algorithm results in better misclassification rate over various UCI data repository datasets. |
| Researcher Affiliation | Academia | Department of Electrical Engineering, Stanford University, Stanford, CA 94305. |
| Pseudocode | Yes | Algorithm 1 Robust R enyi Feature Selection |
| Open Source Code | No | The paper does not provide any explicit statement about releasing its source code or a link to a code repository for its methodology. |
| Open Datasets | Yes | We evaluated the performance of the R enyi classifiers eδ and eδ map on five different binary classification datasets from the UCI machine learning data repository. |
| Dataset Splits | No | The results are averaged over 100 Monte Carlo runs each using 70% of the data for training and the rest for testing. |
| Hardware Specification | No | The paper discusses training times but does not specify the exact hardware (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Matlab SVM command' but does not provide specific version numbers for Matlab or any other software dependencies. |
| Experiment Setup | Yes | The value of λridge and λ is determined through cross validation. |