Self-organizing maps and probabilistic neural networks

0410331 20240903170411.920060210235959.9 Self-organizing maps and probabilistic neural networks 9 s. cav_un_epca*02903211210-0552Neural Network World103 (2000)407-415Ústav informatiky AV ČR, v. v. i. cav_un_auth*0101091 Grim Jiří UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. 09K 12B IAA2075703 GA AV CZ cav_un_auth*0218731 VS96063 MŠMT CZ cav_un_auth*0025066 KSK1075601 GA AV CZ cav_un_auth*0027435 AV0Z1075907 The self-organizing map (SOM) algorithm for training of artificial neural networks is shown to be closely related to a sequential modification of EM algorithm for maximum-likelihood estimation of finite mixtures. Theestablished correspondence provides a helpful theoretical basis for interpretation of the properties of SOM algorithm and for the choice of involved parameters. BB RO http://hdl.handle.net/11104/0003514 UTIA-B 20000047 2000 cav_un_epca*0290321 Neural Network World 1210-0552 Roč. 10 č. 3 2000 407 415 Ústav informatiky AV ČR, v. v. i.