Aprendizaje supervisado de funciones de distancia: estado del arte
The selection of a suitable distance function is fundamental to the instance-based learning algorithms. Such distance function influences the success or failure of these algorithms. Recently it has been shown that even a simple linear transformation of the input attributes can lead to significant im...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | Spanish |
| Published: |
Universidad de Ciencias Informáticas
2015-04-01
|
| Series: | Revista Cubana de Ciencias Informáticas |
| Subjects: | |
| Online Access: | https://rcci.uci.cu/index.php?journal=rcci&page=article&op=view&path[]=1014&path[]=338 |
| _version_ | 1811198166022750208 |
|---|---|
| author | Bac Nguyen Cong Jorge Luis Rivero Pérez Carlos Morell |
| author_facet | Bac Nguyen Cong Jorge Luis Rivero Pérez Carlos Morell |
| author_sort | Bac Nguyen Cong |
| collection | DOAJ |
| description | The selection of a suitable distance function is fundamental to the instance-based learning algorithms. Such distance function influences the success or failure of these algorithms. Recently it has been shown that even a simple linear transformation of the input attributes can lead to significant improvements in classification algorithms as k-Nearest Neighbour (k-NN). One of the main applications of these algorithms is in the hybridization with instance-based learning algorithms and in that sense learning a distance metric for the application at hand and not using a general distance function; which has been shown to improve the learning results. This article presents an overview of distance metric learning, and it is modeled as an optimization problem. It then discusses different approaches to learning from the availability of information in the form of restrictions, focusing on supervised approach, and under it the global and local ones. Further models and strategies of the most representative algorithms of each approach are described. |
| first_indexed | 2024-04-12T01:27:14Z |
| format | Article |
| id | doaj.art-4981d399b1c042e295c76f32c0e7604d |
| institution | Directory Open Access Journal |
| issn | 1994-1536 2227-1899 |
| language | Spanish |
| last_indexed | 2024-04-12T01:27:14Z |
| publishDate | 2015-04-01 |
| publisher | Universidad de Ciencias Informáticas |
| record_format | Article |
| series | Revista Cubana de Ciencias Informáticas |
| spelling | doaj.art-4981d399b1c042e295c76f32c0e7604d2022-12-22T03:53:36ZspaUniversidad de Ciencias InformáticasRevista Cubana de Ciencias Informáticas1994-15362227-18992015-04-01921428Aprendizaje supervisado de funciones de distancia: estado del arteBac Nguyen Cong0Jorge Luis Rivero Pérez1Carlos Morell2Universidad Central “Marta Abreu” de las Villas. Carretera Camajuaní, km 5 ½. Santa Clara, Villa Clara, Cuba. Universidad de Cienfuegos “Carlos Rafael Rodríguez”. Carretera a Rodas. Km. 4. Cienfuegos, Cuba. Universidad Central “Marta Abreu” de las Villas. Carretera Camajuaní, km 5 ½. Santa Clara, Villa Clara, Cuba. The selection of a suitable distance function is fundamental to the instance-based learning algorithms. Such distance function influences the success or failure of these algorithms. Recently it has been shown that even a simple linear transformation of the input attributes can lead to significant improvements in classification algorithms as k-Nearest Neighbour (k-NN). One of the main applications of these algorithms is in the hybridization with instance-based learning algorithms and in that sense learning a distance metric for the application at hand and not using a general distance function; which has been shown to improve the learning results. This article presents an overview of distance metric learning, and it is modeled as an optimization problem. It then discusses different approaches to learning from the availability of information in the form of restrictions, focusing on supervised approach, and under it the global and local ones. Further models and strategies of the most representative algorithms of each approach are described.https://rcci.uci.cu/index.php?journal=rcci&page=article&op=view&path[]=1014&path[]=338classificationdistance metric learningk-Nearest Neighbours |
| spellingShingle | Bac Nguyen Cong Jorge Luis Rivero Pérez Carlos Morell Aprendizaje supervisado de funciones de distancia: estado del arte Revista Cubana de Ciencias Informáticas classification distance metric learning k-Nearest Neighbours |
| title | Aprendizaje supervisado de funciones de distancia: estado del arte |
| title_full | Aprendizaje supervisado de funciones de distancia: estado del arte |
| title_fullStr | Aprendizaje supervisado de funciones de distancia: estado del arte |
| title_full_unstemmed | Aprendizaje supervisado de funciones de distancia: estado del arte |
| title_short | Aprendizaje supervisado de funciones de distancia: estado del arte |
| title_sort | aprendizaje supervisado de funciones de distancia estado del arte |
| topic | classification distance metric learning k-Nearest Neighbours |
| url | https://rcci.uci.cu/index.php?journal=rcci&page=article&op=view&path[]=1014&path[]=338 |
| work_keys_str_mv | AT bacnguyencong aprendizajesupervisadodefuncionesdedistanciaestadodelarte AT jorgeluisriveroperez aprendizajesupervisadodefuncionesdedistanciaestadodelarte AT carlosmorell aprendizajesupervisadodefuncionesdedistanciaestadodelarte |