At each step in its search, a greedy algorithm makes the best decision it can at the time and continues without backtracking. For example, an algorithm may perform a general-to-specific search and at each step, commits itself to the specialization that best fits that training data, so far. It continues without backtracking to change any of its decisions. Greedy algorithms are used in many machine-learning algorithms, including decision tree learning (Breiman et al. 1984; Quinlan 1993) and rule learning algorithms, such as sequential covering.
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Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth International Group, Belmont
Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Mateo
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Sammut, C. (2017). Greedy Search. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_353
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