Automated Learning: An Implementation of The A* Search Algorithm over The Random Base Functions
This is an incremental method for automated learning in data analysis, potentially useful for researchers or practitioners needing efficient function approximation.
The paper tackles the problem of finding a set of base functions to capture the leading behavior of a dataset, using an A* search algorithm combined with gradient descent for parameter optimization, and shows results through plots comparing extrapolation with unseen data.
This letter explains an algorithm for finding a set of base functions. The method aims to capture the leading behavior of the dataset in terms of a few base functions. Implementation of the A-star search will help find these functions, while the gradient descent optimizes the parameters of the functions at each search step. We will show the resulting plots to compare the extrapolation with the unseen data.