. "Dolov\u00E1n\u00ED v textech popisuj\u00EDc\u00EDch v\u00FDjezdy hasi\u010Dsk\u00E9ho z\u00E1chrann\u00E9ho sboru"@cs . "Dolov\u00E1n\u00ED v textech popisuj\u00EDc\u00EDch v\u00FDjezdy hasi\u010Dsk\u00E9ho z\u00E1chrann\u00E9ho sboru" . "P(LA09016), S, Z(MSM0021622418)" . "RIV/00216224:14330/10:00046691" . "machine learning; text mining; fireman brigade; text classification; text preprocessing"@en . "Popel\u00EDnsk\u00FD, Lubom\u00EDr" . "Text mining in reports on incidents with a fire brigade intervention"@en . "G\u00E9ryk, Jan" . "Ostravsk\u00E1 Univerzita v Ostrav\u011B" . . . . . . . "RIV/00216224:14330/10:00046691!RIV11-MSM-14330___" . "4"^^ . . . "Text mining in reports on incidents with a fire brigade intervention"@en . "\u010Cl\u00E1nek se zab\u00FDv\u00E1 problematikou dohled\u00E1v\u00E1n\u00ED text\u016F na internetu dle informac\u00ED o v\u00FDjezdech hasi\u010Dsk\u00E9ho z\u00E1chrann\u00E9ho sboru a n\u00E1slednou klasifikac\u00ED z\u00EDskan\u00FDch text\u016F do r\u016Fzn\u00FDch kategori\u00ED. Popisujeme metodu sb\u011Bru text\u016F a metodu dolov\u00E1n\u00ED ze z\u00EDskan\u00E9 textov\u00E9 informace. Nejvy\u0161\u0161\u00ED celkov\u00E1 spr\u00E1vnost klasifikace dos\u00E1hla 86 %."@cs . "Dolov\u00E1n\u00ED v textech popisuj\u00EDc\u00EDch v\u00FDjezdy hasi\u010Dsk\u00E9ho z\u00E1chrann\u00E9ho sboru" . "2010-01-01+01:00"^^ . . "Mikulov" . . . "Ostrava" . . . "254982" . . "\u010Cl\u00E1nek se zab\u00FDv\u00E1 problematikou dohled\u00E1v\u00E1n\u00ED text\u016F na internetu dle informac\u00ED o v\u00FDjezdech hasi\u010Dsk\u00E9ho z\u00E1chrann\u00E9ho sboru a n\u00E1slednou klasifikac\u00ED z\u00EDskan\u00FDch text\u016F do r\u016Fzn\u00FDch kategori\u00ED. Popisujeme metodu sb\u011Bru text\u016F a metodu dolov\u00E1n\u00ED ze z\u00EDskan\u00E9 textov\u00E9 informace. Nejvy\u0161\u0161\u00ED celkov\u00E1 spr\u00E1vnost klasifikace dos\u00E1hla 86 %." . . "3"^^ . "978-80-7368-424-2" . . . "14330" . . . "3"^^ . "This article deals with searching texts on the internet based on the information obtained from the firemen database. The main goal is to classify such texts into several categories that correspond to the type of incident. We describe a method for text collection and then mining in those texts. The overall classification accuracy reached 86 %."@en . "Dolov\u00E1n\u00ED v textech popisuj\u00EDc\u00EDch v\u00FDjezdy hasi\u010Dsk\u00E9ho z\u00E1chrann\u00E9ho sboru"@cs . "Bayer, Jaroslav" . "[46650F20A235]" . . "Proceedings of the Annual Database Conference - Datakon 2010" . . .