Automated Learning: An Implementation of The A* Search Algorithm over The Random Base Functions

TL;DR

This paper presents an algorithm that uses A* search combined with gradient descent to identify a small set of base functions that effectively model and extrapolate dataset behavior, demonstrated through visual comparisons.

Contribution

It introduces a novel method integrating A* search with gradient descent for selecting base functions to model data behavior.

Findings

Effective identification of base functions for datasets

Improved extrapolation performance on unseen data

Visualization of model predictions versus actual data

Abstract

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.