Registro completo de metadatos
| Campo DC | Valor | Lengua/Idioma |
|---|---|---|
| dc.provenance | Universidad de San Andrés | - |
| dc.creator | Fraiman, Ricardo | - |
| dc.creator | Justel, Ana | - |
| dc.creator | Svarc, Marcela | - |
| dc.date.accessioned | 2018-05-04T16:44:40Z | - |
| dc.date.accessioned | 2018-05-14T17:54:11Z | - |
| dc.date.available | 2018-05-04T16:44:40Z | - |
| dc.date.available | 2018-05-14T17:54:11Z | - |
| dc.date.issued | 2011-09-19 | - |
| dc.identifier.uri | http://10.0.0.11:8080/jspui/handle/bnmm/55037 | - |
| dc.description | ( Documento de trabajo ; ; 53 ) Título tomado de la pantalla de presentación (visto 19 de septiembre de 2011). Junio 2008". Incluye referencias bibliográficas. | - |
| dc.description | We introduce a new procedure for pattern recognition, based on the concepts of random projections and nearest neighbors. It can be thought as an improvement of the classical nearest neighbors classification rules. Besides the concept of neighbors we introduce the notion of district, a larger set which will be projected. Then we apply one dimensional k-NN methods to the projected data on randomly selected directions. In this way we are able to provide a method with some robustness properties and more accurate to handle high dimensional data. The procedure is also universally consistent. We challenge the method with the Isolet data where we obtain a very high classification score. | - |
| dc.language | en | - |
| dc.source.uri | http://hdl.handle.net/10908/553 | - |
| dc.subject | Multivariate analysis | - |
| dc.subject | Robust statistics | - |
| dc.subject | Pattern perception | - |
| dc.title | Pattern recognition via projection – based k – NN rules | - |
| dc.type | Working Paper | - |
| Aparece en las colecciones: | Universidad de San Andrés | |
Ficheros en este ítem:
No hay ficheros asociados a este ítem.