1 resultado para Multiplier
em Nottingham eTheses
Filtro por publicador
- Repository Napier (1)
- Aberdeen University (2)
- Academic Research Repository at Institute of Developing Economies (1)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (2)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (1)
- Aquatic Commons (1)
- ArchiMeD - Elektronische Publikationen der Universität Mainz - Alemanha (1)
- Archive of European Integration (2)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (3)
- Aston University Research Archive (8)
- Biblioteca de Teses e Dissertações da USP (2)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (2)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (2)
- Biblioteca Digital de Teses e Dissertações Eletrônicas da UERJ (2)
- Bioline International (1)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (4)
- Bulgarian Digital Mathematics Library at IMI-BAS (11)
- CaltechTHESIS (3)
- Cambridge University Engineering Department Publications Database (10)
- CentAUR: Central Archive University of Reading - UK (9)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (7)
- Cochin University of Science & Technology (CUSAT), India (7)
- Comissão Econômica para a América Latina e o Caribe (CEPAL) (3)
- Dalarna University College Electronic Archive (2)
- Deakin Research Online - Australia (26)
- Digital Commons at Florida International University (7)
- Doria (National Library of Finland DSpace Services) - National Library of Finland, Finland (1)
- Duke University (1)
- eResearch Archive - Queensland Department of Agriculture; Fisheries and Forestry (1)
- Gallica, Bibliotheque Numerique - Bibliothèque nationale de France (French National Library) (BnF), France (1)
- Greenwich Academic Literature Archive - UK (1)
- Helda - Digital Repository of University of Helsinki (5)
- Indian Institute of Science - Bangalore - Índia (37)
- Instituto Politécnico de Santarém (1)
- Instituto Politécnico do Porto, Portugal (1)
- Lume - Repositório Digital da Universidade Federal do Rio Grande do Sul (1)
- Massachusetts Institute of Technology (2)
- National Center for Biotechnology Information - NCBI (1)
- Nottingham eTheses (1)
- Plymouth Marine Science Electronic Archive (PlyMSEA) (2)
- Publishing Network for Geoscientific & Environmental Data (1)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (33)
- Queensland University of Technology - ePrints Archive (18)
- RDBU - Repositório Digital da Biblioteca da Unisinos (1)
- Repositório Científico do Instituto Politécnico de Lisboa - Portugal (1)
- Repositório Científico do Instituto Politécnico de Santarém - Portugal (1)
- Repositório digital da Fundação Getúlio Vargas - FGV (7)
- Repositório Institucional da Universidade de Aveiro - Portugal (2)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (30)
- Savoirs UdeS : plateforme de diffusion de la production intellectuelle de l’Université de Sherbrooke - Canada (1)
- Universidad de Alicante (4)
- Universidad del Rosario, Colombia (1)
- Universidad Politécnica de Madrid (6)
- Universidade Complutense de Madrid (1)
- Universidade Federal do Pará (3)
- Universidade Federal do Rio Grande do Norte (UFRN) (2)
- Universidade Metodista de São Paulo (2)
- Université de Montréal (3)
- Université de Montréal, Canada (21)
- Université Laval Mémoires et thèses électroniques (3)
- University of Connecticut - USA (1)
- University of Michigan (21)
- University of Queensland eSpace - Australia (3)
- WestminsterResearch - UK (4)
Resumo:
As an immune-inspired algorithm, the Dendritic Cell Algorithm (DCA), produces promising performance in the field of anomaly detection. This paper presents the application of the DCA to a standard data set, the KDD 99 data set. The results of different implementation versions of the DCA, including antigen multiplier and moving time windows, are reported. The real-valued Negative Selection Algorithm (NSA) using constant-sized detectors and the C4.5 decision tree algorithm are used, to conduct a baseline comparison. The results suggest that the DCA is applicable to KDD 99 data set, and the antigen multiplier and moving time windows have the same effect on the DCA for this particular data set. The real-valued NSA with contant-sized detectors is not applicable to the data set. And the C4.5 decision tree algorithm provides a benchmark of the classification performance for this data set.