| En/De/Fr/It- (first 9 out of 100 sentences)
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Srpski - (prvih 9 od 100 rečenica)
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| n1 | Dima Alberg | n1 | Dima Alberg |
| n2 | Department of Industrial Engineering and Management, SCE - Shamoon College of Engineering, Beer-Sheva, Israel | n2 | Odsek za industrijski inženjering i upravljanje, SCE - Shamoon College of Engineering, Beer-Sheva, Israel |
| n3 | Incremental Interval Regression Tree Learning with Mean Variance Numerical Data Streams | n3 | Inkrementalno izucavanje intervala primenom drveta regresije sa srednjom varijansom numerickih tokova podataka |
| n4 | XIII International Simposium SymOrg 2012, 05-09 June 2012, Zlatibor, Serbia | n4 | XIII Internacionalni Simpozijum SymOrg 2012, 05.-09. Jun 2012, Zlatibor, Srbija |
| n5 | In this paper, we present a novel method for interval regression tree incremental learning with mean variance patterned numerical data streams. | n5 | U ovom radu predstavljamo novi model inkrementalnog ucenja primenom drveta regresije koristeci numericke tokove podataka struktuirane pomocu srednje varijanse. |
| n6 | The 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. | n6 | Predloženi 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. |
| n7 | The algorithm main properties are time - based incremental mean variance tree induction algorithm accompanying novel time resolution and outliers detection mechanism. | n7 | Glavna 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. |
| n8 | Results 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. | n8 | Rezultati 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. |
| n9 | Keywords: prediction, regression tree, incremental learning, data stream mining, interval prediction | n9 | Kljucne reci: predvidanje, drvo regresione analize, inkrementalno ucenje, analiza tokova podataka, predvidanje intervala |