Maximum Margin Multiclass Nearest Neighbors
Authors: Aryeh Kontorovich, Roi Weiss
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We develop a general framework for marginbased multicategory classification in metric spaces. The basic work-horse is a marginregularized version of the nearest-neighbor classifier. We prove generalization bounds that match the state of the art in sample size n and significantly improve the dependence on the number of classes k. |
| Researcher Affiliation | Academia | Aryeh Kontorovich KARYEH@CS.BGU.AC.IL Department of Computer Science, Ben-Gurion University, Beer Sheva 84105, ISRAEL Roi Weiss ROIWEI@CS.BGU.AC.IL Department of Computer Science, Ben-Gurion University, Beer Sheva 84105, ISRAEL |
| Pseudocode | No | The paper describes algorithmic steps and procedures in text (e.g., in Section 4 'Algorithm') but does not include any formally labeled pseudocode blocks or algorithms. |
| Open Source Code | No | The paper does not provide any statements or links indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper that focuses on mathematical proofs and algorithmic analysis. It does not use or refer to any specific publicly available datasets for training or evaluation. |
| Dataset Splits | No | This is a theoretical paper and does not involve experimental validation on datasets, thus no training, validation, or test splits are described. |
| Hardware Specification | No | This is a theoretical paper and does not report on conducted experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | This is a theoretical paper and does not involve software implementation details, therefore no specific software dependencies or versions are mentioned. |
| Experiment Setup | No | This is a theoretical paper and does not report on conducted experiments, therefore no experimental setup details, hyperparameters, or training configurations are provided. |