Rate of Convergence of $k$-Nearest-Neighbor Classification Rule
Authors: Maik Döring, László Györfi, Harro Walk
JMLR 2017 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The main aim of this paper is to show tight upper bounds on the excess error probability E{L(gn,k)} L of the k-nearest-neighbor classification rule gn,k. We split Theorem 1 into two lemmas such that Lemma 2 is on the estimation error, while Lemma 3 is on the approximation error. The proofs of these lemmas are in Section 4. In this section we present the proofs of Lemmas 2 and 3, hence Theorem 1, and of Proposition 4, and of Theorems 5 and 6. |
| Researcher Affiliation | Academia | Maik D oring EMAIL Institute of Applied Mathematics and Statistics University of Hohenheim, 70599 Stuttgart, Germany, Max Rubner Institute,76131 Karlsruhe, Germany L aszl o Gy orfi EMAIL Department of Computer Science and Information Theory Budapest University of Technology and Economics 1111 Budapest, Hungary Harro Walk EMAIL Institute of Stochastic and Applications University of Stuttgart 70049 Stuttgart, Germany |
| Pseudocode | No | The paper describes the k-nearest-neighbor classification rule and related theoretical concepts mathematically, but it does not present any structured pseudocode or algorithm blocks. The description of the rule is given in paragraph form. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code, nor does it provide links to any code repositories or supplementary materials containing code for the described methodology. |
| Open Datasets | No | The paper is theoretical in nature, focusing on the rate of convergence of a classification rule. It does not conduct experiments on specific datasets or mention the use of any publicly available or open datasets. |
| Dataset Splits | No | The paper is purely theoretical and does not involve empirical evaluations using datasets. Therefore, there is no mention of training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is a theoretical work focusing on mathematical proofs and analyses of convergence rates. It does not describe any experimental setup or specify hardware used for computations. |
| Software Dependencies | No | The paper is theoretical and does not detail any experimental implementation. Consequently, it does not list any software dependencies or specific version numbers of libraries or tools. |
| Experiment Setup | No | The paper is a theoretical study on the rate of convergence of a classification rule, presenting mathematical proofs and conditions. It does not describe any experimental setup, hyperparameters, or system-level training settings. |