There have been different strategies to improve the performance of a machine learning model, e.g., increasing the depth, width, and/or nonlinearity of the model, and using ensemble learning to aggregate multiple base/weak learners in parallel or in series. We propose a novel strategy called patch learning (PL) for this problem. It consists of three steps: 1) train an initial global model using all training data; 2) identify from the initial global model the patches, which contribute the most to the learning error, and train a (local) patch model for each such patch; and, 3) update the global model using training data that do not fall into any patch. To use a PL model, we first determine if the input falls into any patch. If yes, then the corresponding patch model is used to compute the output. Otherwise, the global model is used. A function illustrating PL is included.
Cite As
David Wu (2024). Patch Learning (PL) (https://www.mathworks.com/matlabcentral/fileexchange/71568-patch-learning-pl), MATLAB Central File Exchange. Retrieved .
Dongrui Wu (2019). Patch Learning (https://www.mathworks.com/matlabcentral/fileexchange/<...>), MATLAB Central File Exchange. Retrieved May 17, 2019.
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