A Kernelized Unified Framework for Domain Adaptation

A Kernelized Unified Framework for Domain Adaptation

The performance of the supervised learning algorithms such as k-nearest neighbor (k-NN) depends on the labeled data. For some applications (Target Domain), obtaining such labeled data is very expensive and labor-intensive. In a real-world scenario, the possibility of some other related application (...

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Bibliographic Details
Main Authors: Rakesh Kumar Sanodiya, Jimson Mathew, Biju Paul, Bijoy A. Jose
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Transfer learning
domain adaptation
manifold diffusion
unsupervised learning
kernelization
classification
Online Access:https://ieeexplore.ieee.org/document/8935486/

Internet

https://ieeexplore.ieee.org/document/8935486/

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