1 resultado para Decision Trees
em Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España
Filtro por publicador
- JISC Information Environment Repository (1)
- Repository Napier (5)
- Aberystwyth University Repository - Reino Unido (7)
- Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España (1)
- Aquatic Commons (9)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (10)
- Aston University Research Archive (5)
- Biblioteca de Teses e Dissertações da USP (1)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (4)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (1)
- Biblioteca Digital de la Universidad Católica Argentina (1)
- Biblioteca Digital de Teses e Dissertações Eletrônicas da UERJ (1)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (3)
- Boston University Digital Common (5)
- Bucknell University Digital Commons - Pensilvania - USA (1)
- Bulgarian Digital Mathematics Library at IMI-BAS (5)
- CaltechTHESIS (7)
- Cambridge University Engineering Department Publications Database (121)
- CentAUR: Central Archive University of Reading - UK (18)
- Center for Jewish History Digital Collections (2)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (29)
- CiencIPCA - Instituto Politécnico do Cávado e do Ave, Portugal (1)
- Cochin University of Science & Technology (CUSAT), India (3)
- CORA - Cork Open Research Archive - University College Cork - Ireland (6)
- Dalarna University College Electronic Archive (2)
- Deakin Research Online - Australia (29)
- DigitalCommons@The Texas Medical Center (1)
- DRUM (Digital Repository at the University of Maryland) (1)
- Duke University (20)
- eResearch Archive - Queensland Department of Agriculture; Fisheries and Forestry (17)
- Greenwich Academic Literature Archive - UK (10)
- Helda - Digital Repository of University of Helsinki (30)
- Indian Institute of Science - Bangalore - Índia (68)
- Instituto Politécnico de Viseu (1)
- Instituto Politécnico do Porto, Portugal (2)
- Massachusetts Institute of Technology (2)
- Plymouth Marine Science Electronic Archive (PlyMSEA) (3)
- Publishing Network for Geoscientific & Environmental Data (1)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (101)
- Queensland University of Technology - ePrints Archive (384)
- Repositório Alice (Acesso Livre à Informação Científica da Embrapa / Repository Open Access to Scientific Information from Embrapa) (1)
- Repositório Científico da Universidade de Évora - Portugal (1)
- Repositório digital da Fundação Getúlio Vargas - FGV (2)
- Repositório do Centro Hospitalar de Lisboa Central, EPE - Centro Hospitalar de Lisboa Central, EPE, Portugal (1)
- Repositório Institucional da Universidade Federal do Rio Grande - FURG (1)
- Repositorio Institucional de la Universidad de Málaga (1)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (11)
- Repositorio Institucional Universidad de Medellín (1)
- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (1)
- Universidad de Alicante (1)
- Universidad del Rosario, Colombia (1)
- Universidad Politécnica de Madrid (7)
- Universidade de Lisboa - Repositório Aberto (1)
- Universidade Federal do Pará (3)
- Universidade Federal do Rio Grande do Norte (UFRN) (4)
- Universitat de Girona, Spain (3)
- Université de Lausanne, Switzerland (1)
- Université de Montréal, Canada (3)
- Université Laval Mémoires et thèses électroniques (1)
- University of Michigan (3)
- University of Queensland eSpace - Australia (4)
- University of Southampton, United Kingdom (10)
Resumo:
[EN] Indoor position estimation has become an attractive research topic due to growing interest in location-aware services. Nevertheless, satisfying solutions have not been found with the considerations of both accuracy and system complexity. From the perspective of lightweight mobile devices, they are extremely important characteristics, because both the processor power and energy availability are limited. Hence, an indoor localization system with high computational complexity can cause complete battery drain within a few hours. In our research, we use a data mining technique named boosting to develop a localization system based on multiple weighted decision trees to predict the device location, since it has high accuracy and low computational complexity.