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 Alberg D., 2012, No. 63, ID: 7.2012.63.3[About]



En/De/Fr/It- (first 9 out of 100 sentences) [pdf] Srpski - (prvih 9 od 100 rečenica) [pdf]
n1Dima Alberg n1Dima Alberg
n2Department of Industrial Engineering and Management, SCE - Shamoon College of Engineering, Beer-Sheva, Israel n2Odsek za industrijski inženjering i upravljanje, SCE - Shamoon College of Engineering, Beer-Sheva, Israel
n3Incremental Interval Regression Tree Learning with Mean Variance Numerical Data Streams n3Inkrementalno izucavanje intervala primenom drveta regresije sa srednjom varijansom numerickih tokova podataka
n4XIII International Simposium SymOrg 2012, 05-09 June 2012, Zlatibor, Serbia n4XIII Internacionalni Simpozijum SymOrg 2012, 05.-09. Jun 2012, Zlatibor, Srbija
n5In this paper, we present a novel method for interval regression tree incremental learning with mean variance patterned numerical data streams. n5U ovom radu predstavljamo novi model inkrementalnog ucenja primenom drveta regresije koristeci numericke tokove podataka struktuirane pomocu srednje varijanse.
n6The proposed Mean Variance Interval Regression Tree (MVIRT) algorithm transforms continuous temporal data into two statistical moments according to a user-specified time resolution and builds a regression model tree for estimating the prediction interval of the target variable. n6Predloženi MVIRT (Mean Variance Interval Regression Tree) algoritam pretvara kontinuirane vremenske podatke u dva statisticka momenta u skladu sa vremenom koje je korisnik odredio i gradi model stabla regresije za procenu intervala predvidljivosti ciljne varijable.
n7The algorithm main properties are time - based incremental mean variance tree induction algorithm accompanying novel time resolution and outliers detection mechanism. n7Glavna osobenost ovog algoritma jeste vremenski odreden algoritam za indukciju inkrementalne varijanse koji se kombinuje sa novom rezolucijom vremena i mehanizmom za detekciju podataka koji odstupaju od uobicajenih.
n8Results of real world data stream show that the MVIRT algorithm produces more accurate and easily interpretable prediction models than other state-of-the-art batch incremental model tree methods. n8Rezultati tokova podataka u realnom vremenu pokazuju da se primenom MVIRT algoritma dobijaju precizniji modeli predvidanja koje je lakše tumaciti u poredenju sa drugim metodamama za serijsku obradu inkrementalnog modela drveta koji su danas u upotrebi.
n9Keywords: prediction, regression tree, incremental learning, data stream mining, interval prediction n9Kljucne reci: predvidanje, drvo regresione analize, inkrementalno ucenje, analiza tokova podataka, predvidanje intervala