Non-parametric Nearest Neighbor with Local Adaptation
The k-Nearest Neighbor algorithm (k-NN) uses a classification criterion that depends on the parameter k. Usually, the value of this parameter must be determined by the user. In this paper we present an algorithm based on the NN technique that does not take the value of k from the user. Our approach...
| Autores: | , , |
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| Tipo de recurso: | capítulo de libro |
| Estado: | Versión publicada |
| Fecha de publicación: | 2001 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/39147 |
| Acceso en línea: | http://hdl.handle.net/11441/39147 https://doi.org/10.1007/3-540-45329-6_6 |
| Access Level: | acceso abierto |
| Palabra clave: | Artificial Intelligence (incl. Robotics) |
| Sumario: | The k-Nearest Neighbor algorithm (k-NN) uses a classification criterion that depends on the parameter k. Usually, the value of this parameter must be determined by the user. In this paper we present an algorithm based on the NN technique that does not take the value of k from the user. Our approach evaluates values of k that classified the training examples correctly and takes which classified most examples. As the user does not take part in the election of the parameter k, the algorithm is non-parametric. With this heuristic, we propose an easy variation of the k-NN algorithm that gives robustness with noise present in data. Summarized in the last section, the experiments show that the error rate decreases in comparison with the k-NN technique when the best k for each database has been previously obtained. |
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