Scalable Large-Margin Mahalanobis Distance Metric Learning
- Author
- CHUNHUA SHEN1 2 ; KIM, Junae2 ; LEI WANG2
[1] NICTA, Canberra Research Laboratory, Canberra, ACT 2601, Australia
[2] Australian National University, Canberra, ACT 0200, Australia - Source
IEEE transactions on neural networks. 2010, Vol 21, Num 9, pp 1524-1530, 7 p ; ref : 27 ref
- CODEN
- ITNNEP
- ISSN
- 1045-9227
- Scientific domain
- Electronics; Computer science; Psychology, psychopathology, psychiatry
- Publisher
- Institute of Electrical and Electronics Engineers, New York, NY
- Publication country
- United States
- Document type
- Article
- Language
- English
- Author keyword
- Distance metric learning Mahalanobis distance large-margin nearest neighbor semidefinite optimization
- Keyword (fr)
- Analyse donnée Distance Observation aberrante Optimisation Plus proche voisin Réseau neuronal
- Keyword (en)
- Data analysis Distance Outlier Optimization Nearest neighbour Neural network
- Keyword (es)
- Análisis datos Distancia Observación aberrante Optimización Vecino más cercano Red neuronal
- Classification
- Pascal
- 001 Exact sciences and technology / 001D Applied sciences / 001D02 Computer science; control theory; systems / 001D02B Software / 001D02B07 Memory organisation. Data processing / 001D02B07B Data processing. List processing. Character string processing
- Pascal
- 001 Exact sciences and technology / 001D Applied sciences / 001D02 Computer science; control theory; systems / 001D02C Artificial intelligence / 001D02C06 Connectionism. Neural networks
- Discipline
- Computer science : theoretical automation and systems
- Origin
- Inist-CNRS
- Database
- PASCAL
- INIST identifier
- 23252803
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
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DOI
10.1109/TNN.2010.2052630 
https://dx.doi.org/10.1109/TNN.2010.2052630
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