Unsupervised Transfer Learning via Relative Distance Comparisons

Unsupervised Transfer Learning via Relative Distance Comparisons

Primitive machine learning method such as Support Vector Machine (SVM) or k-Nearest Neighbor (k-NN) faces a major challenge when its training and test data is distributed with large-scale variations in lighting conditions, color, backgrounds, size, etc. The variation may be because training and test...

Täydet tiedot

Bibliografiset tiedot
Päätekijät: Rakesh Kumar Sanodiya, Leehter Yao
Aineistotyyppi: Artikkeli
Kieli:English
Julkaistu: IEEE 2020-01-01
Sarja:IEEE Access
Aiheet:
Unsupervised discrimenent analysis
domain adaptation
classification
metric learning
relative distance comparison
kernelization
Linkit:https://ieeexplore.ieee.org/document/9117096/

Internet

https://ieeexplore.ieee.org/document/9117096/

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