caa a22 4500 475800796 CHVBK 20180406123736.0 cr unu---uuuuu 170329e20000801xx s 000 0 eng 10.1023/A:1009665713438 doi (NATIONALLICENCE)springer-10.1023/A:1009665713438 Al-Mashouq Khalid EE Department, King Saud University, 11421, Riyadh, Saudi Arabia aut Clustered Recursive Branching Network [Elektronische Daten] [Khalid Al-Mashouq] Recursive branching network (RBN) was proposed in [1] to solve linearly non-separable problems using output-coded perceptrons. It relies on splitting the training patterns, at random, between parallel perceptrons. However, the random splitting mechanism can trap the perceptron with conflicting patterns. Optimizing the splitting methods, through clustering, is proposed here to ensure meaningful way of distributing the patterns between the perceptrons. We propose four splitting methods which use different similarity measures between patterns. We examine these methods on five standard data sets. In general, these methods enhance the performance of RBN and in many cases contribute to reducing the network complexity compared with random-splitting RBN. Kluwer Academic Publishers, 2000 recursive branching network nationallicence structured perceptrons nationallicence data clustering nationallicence Neural Processing Letters Kluwer Academic Publishers 12/1(2000-08-01), 59-69 1370-4621 12:1<59 2000 12 11063 https://doi.org/10.1023/A:1009665713438 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1023/A:1009665713438 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Al-Mashouq Khalid EE Department, King Saud University, 11421, Riyadh, Saudi Arabia aut NATIONALLICENCE 773 0- Neural Processing Letters Kluwer Academic Publishers 12/1(2000-08-01), 59-69 1370-4621 12:1<59 2000 12 11063 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer