caa a22 4500 475800842 CHVBK 20180406123736.0 cr unu---uuuuu 170329e20001201xx s 000 0 eng 10.1023/A:1026530200837 doi (NATIONALLICENCE)springer-10.1023/A:1026530200837 Using a Neural Network to Approximate an Ensemble of Classifiers [Elektronische Daten] [X. Zeng, T. Martinez] Several methods (e.g., Bagging, Boosting) of constructing and combining an ensemble of classifiers have recently been shown capable of improving accuracy of a class of commonly used classifiers (e.g., decision trees, neural networks). The accuracy gain achieved, however, is at the expense of a higher requirement for storage and computation. This storage and computation overhead can decrease the utility of these methods when applied to real-world situations. In this Letter, we propose a learning approach which allows a single neural network to approximate a given ensemble of classifiers. Experiments on a large number of real-world data sets show that this approach can substantially save storage and computation while still maintaining accuracy similar to that of the entire ensemble. Kluwer Academic Publishers, 2000 approximator nationallicence bagging nationallicence boosting nationallicence ensemble of classifiers nationallicence neural networks nationallicence Zeng X. Computer Science Department, Brigham Young University, 3366 TMCB, 84602, Provo, UT, USA aut Martinez T. Computer Science Department, Brigham Young University, 3366 TMCB, 84602, Provo, UT, USA aut Neural Processing Letters Kluwer Academic Publishers 12/3(2000-12-01), 225-237 1370-4621 12:3<225 2000 12 11063 https://doi.org/10.1023/A:1026530200837 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1023/A:1026530200837 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zeng X. Computer Science Department, Brigham Young University, 3366 TMCB, 84602, Provo, UT, USA aut NATIONALLICENCE 700 1- Martinez T. Computer Science Department, Brigham Young University, 3366 TMCB, 84602, Provo, UT, USA aut NATIONALLICENCE 773 0- Neural Processing Letters Kluwer Academic Publishers 12/3(2000-12-01), 225-237 1370-4621 12:3<225 2000 12 11063 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer