1 resultado para 280000 Information, Computing and Communication Sciences
em Nottingham eTheses
Filtro por publicador
- JISC Information Environment Repository (3)
- Repository Napier (1)
- Aberdeen University (3)
- Aberystwyth University Repository - Reino Unido (3)
- Academic Archive On-line (Jönköping University; Sweden) (1)
- Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España (1)
- Adam Mickiewicz University Repository (1)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (3)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (2)
- Applied Math and Science Education Repository - Washington - USA (3)
- Aquatic Commons (4)
- Archive of European Integration (72)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (3)
- Aston University Research Archive (24)
- Biblioteca Digital | Sistema Integrado de Documentación | UNCuyo - UNCUYO. UNIVERSIDAD NACIONAL DE CUYO. (1)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (1)
- Bioline International (1)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (39)
- Boston University Digital Common (4)
- Brock University, Canada (3)
- Bulgarian Digital Mathematics Library at IMI-BAS (9)
- CaltechTHESIS (1)
- Cambridge University Engineering Department Publications Database (22)
- CentAUR: Central Archive University of Reading - UK (45)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (4)
- Cochin University of Science & Technology (CUSAT), India (4)
- Comissão Econômica para a América Latina e o Caribe (CEPAL) (54)
- CORA - Cork Open Research Archive - University College Cork - Ireland (2)
- Corvinus Research Archive - The institutional repository for the Corvinus University of Budapest (2)
- CUNY Academic Works (4)
- Dalarna University College Electronic Archive (2)
- Deakin Research Online - Australia (109)
- Department of Computer Science E-Repository - King's College London, Strand, London (3)
- Digital Commons - Michigan Tech (1)
- Digital Commons at Florida International University (14)
- Digital Peer Publishing (3)
- Digital Repository at Iowa State University (1)
- DigitalCommons@The Texas Medical Center (3)
- DigitalCommons@University of Nebraska - Lincoln (7)
- Doria (National Library of Finland DSpace Services) - National Library of Finland, Finland (2)
- DRUM (Digital Repository at the University of Maryland) (4)
- Duke University (2)
- eScholarship Repository - University of California (2)
- Escola Superior de Educação de Paula Frassinetti (1)
- FAUBA DIGITAL: Repositorio institucional científico y académico de la Facultad de Agronomia de la Universidad de Buenos Aires (3)
- Greenwich Academic Literature Archive - UK (4)
- Helda - Digital Repository of University of Helsinki (9)
- Illinois Digital Environment for Access to Learning and Scholarship Repository (1)
- Indian Institute of Science - Bangalore - Índia (13)
- Institutional Repository of Leibniz University Hannover (1)
- Instituto Politécnico de Bragança (2)
- Instituto Politécnico de Viseu (1)
- Instituto Politécnico do Porto, Portugal (1)
- Iowa Publications Online (IPO) - State Library, State of Iowa (Iowa), United States (1)
- Massachusetts Institute of Technology (3)
- Memoria Académica - FaHCE, UNLP - Argentina (12)
- Ministerio de Cultura, Spain (15)
- National Center for Biotechnology Information - NCBI (5)
- Nottingham eTheses (1)
- Plymouth Marine Science Electronic Archive (PlyMSEA) (2)
- Portal de Revistas Científicas Complutenses - Espanha (2)
- Publishing Network for Geoscientific & Environmental Data (1)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (48)
- Queensland University of Technology - ePrints Archive (96)
- ReCiL - Repositório Científico Lusófona - Grupo Lusófona, Portugal (2)
- Repositorio Académico de la Universidad Nacional de Costa Rica (7)
- Repositório Alice (Acesso Livre à Informação Científica da Embrapa / Repository Open Access to Scientific Information from Embrapa) (1)
- Repositório Científico do Instituto Politécnico de Lisboa - Portugal (3)
- Repositório digital da Fundação Getúlio Vargas - FGV (12)
- Repositório Institucional da Universidade de Aveiro - Portugal (1)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (9)
- Repositorio Institucional Universidad de Medellín (1)
- Royal College of Art Research Repository - Uninet Kingdom (1)
- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (3)
- School of Medicine, Washington University, United States (32)
- Scientific Open-access Literature Archive and Repository (1)
- Universidad de Alicante (3)
- Universidad del Rosario, Colombia (1)
- Universidad Politécnica de Madrid (11)
- Universidade Complutense de Madrid (1)
- Universidade Técnica de Lisboa (1)
- Universitat de Girona, Spain (1)
- Universitätsbibliothek Kassel, Universität Kassel, Germany (2)
- Université de Lausanne, Switzerland (2)
- Université de Montréal (1)
- Université de Montréal, Canada (5)
- University of Canberra Research Repository - Australia (3)
- University of Michigan (62)
- University of Queensland eSpace - Australia (94)
- University of Southampton, United Kingdom (5)
- University of Washington (1)
- WestminsterResearch - UK (3)
- Worcester Research and Publications - Worcester Research and Publications - UK (4)
Resumo:
Analysis of data without labels is commonly subject to scrutiny by unsupervised machine learning techniques. Such techniques provide more meaningful representations, useful for better understanding of a problem at hand, than by looking only at the data itself. Although abundant expert knowledge exists in many areas where unlabelled data is examined, such knowledge is rarely incorporated into automatic analysis. Incorporation of expert knowledge is frequently a matter of combining multiple data sources from disparate hypothetical spaces. In cases where such spaces belong to different data types, this task becomes even more challenging. In this paper we present a novel immune-inspired method that enables the fusion of such disparate types of data for a specific set of problems. We show that our method provides a better visual understanding of one hypothetical space with the help of data from another hypothetical space. We believe that our model has implications for the field of exploratory data analysis and knowledge discovery.