A python implementation of the apdative transfer learning algorithm for Gaussian processes (AT-GP) proposed by Cao et al. in 2010 (Bin Cao, Sinno Jialin Pan, Yu Zhang, Dit-Yan Yeung, Qiang Yang: Adaptive Transfer Learning, in Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI) 2010). The repository also contains code to evaluate the prediction accuracy of AT-GP using the Styblinski-Tang function (https://www.sfu.ca/~ssurjano/stybtang.html) and the Goldstein-Price function (https://www.sfu.ca/~ssurjano/goldpr.html).
The main folder of the repository is AT-GP and it contains the following sub-folders:
Contains the implementation of the ATGP kernel.
Contains the different implementations of the synthetic functions used for the experiments.
Contains data generated from the synthetic functions and used for the experiments.
Contains scripts to plot the result of the different experiments and to plot the synthetic functions.
This project is licensed under the Apache License 2.0. If you would like to see the detailed LICENSE click here.
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