{"?xml":{"@version":"1.0"},"edm:RDF":{"@xmlns:dc":"http://purl.org/dc/elements/1.1/","@xmlns:edm":"http://www.europeana.eu/schemas/edm/","@xmlns:wgs84_pos":"http://www.w3.org/2003/01/geo/wgs84_pos","@xmlns:foaf":"http://xmlns.com/foaf/0.1/","@xmlns:rdaGr2":"http://rdvocab.info/ElementsGr2","@xmlns:oai":"http://www.openarchives.org/OAI/2.0/","@xmlns:owl":"http://www.w3.org/2002/07/owl#","@xmlns:rdf":"http://www.w3.org/1999/02/22-rdf-syntax-ns#","@xmlns:ore":"http://www.openarchives.org/ore/terms/","@xmlns:skos":"http://www.w3.org/2004/02/skos/core#","@xmlns:dcterms":"http://purl.org/dc/terms/","edm:WebResource":[{"@rdf:about":"http://www.dlib.si/stream/URN:NBN:SI:doc-DEY5BFG2/e8e3bb7e-0304-4cee-bdd5-b22f26676fe3/PDF","dcterms:extent":"1212 KB"},{"@rdf:about":"http://www.dlib.si/stream/URN:NBN:SI:doc-DEY5BFG2/66a809c0-2c41-43ea-b54d-5bb661dc06e2/TEXT","dcterms:extent":"29 KB"}],"edm:TimeSpan":{"@rdf:about":"2013-2025","edm:begin":{"@xml:lang":"en","#text":"2013"},"edm:end":{"@xml:lang":"en","#text":"2025"}},"edm:ProvidedCHO":{"@rdf:about":"URN:NBN:SI:doc-DEY5BFG2","dcterms:isPartOf":[{"@rdf:resource":"https://www.dlib.si/details/URN:NBN:SI:spr-MCCAWXYE"},{"@xml:lang":"sl","#text":"Acta silvae et ligni"}],"dcterms:issued":"2017","dc:creator":["Džeroski, Sašo","Jevšenak, Jernej","Levanič, Tom"],"dc:format":[{"@xml:lang":"sl","#text":"številka:114"},{"@xml:lang":"sl","#text":"str. 21-29"}],"dc:identifier":["ISSN:2335-3112","COBISSID_HOST:4998310","URN:URN:NBN:SI:doc-DEY5BFG2"],"dc:language":"sl","dc:publisher":[{"@xml:lang":"sl","#text":"Biotehniška fakulteta, Oddelek za gozdarstvo in obnovljive gozdne vire"},{"@xml:lang":"sl","#text":"Biotehniška fakulteta, Oddelek za lesarstvo"},{"@xml:lang":"sl","#text":"Gozdarski inštitut Slovenije, založba Silvae Slovenica"}],"dc:subject":[{"@xml:lang":"sl","#text":"dendroklimatologija"},{"@xml:lang":"sl","#text":"linearna regresija"},{"@xml:lang":"sl","#text":"modelna drevesa"},{"@xml:lang":"sl","#text":"naključni gozdovi"},{"@xml:lang":"sl","#text":"primerjava metod"},{"@xml:lang":"sl","#text":"strojno učenje"},{"@xml:lang":"sl","#text":"umetne nevronske mreže"},{"@rdf:resource":"http://www.wikidata.org/entity/Q2539"}],"dcterms:temporal":{"@rdf:resource":"2013-2025"},"dc:title":{"@xml:lang":"sl","#text":"Uporaba metod strojnega učenja za preučevanje odnosov med značilnostmi branik in okoljem| On the use of machine learning methods to study the relationships between tree-ring characteristics and the environment|"},"dc:description":[{"@xml:lang":"sl","#text":"Many studies have shown that by using nonlinear methods, the relationship between tree-ring parameters and the environment can be described (modelled) better and in more detail. In our study, (multiple) linear regression (MLR) with four nonlinear machine learning methods are compared: artificial neural networks (ANN), model trees (MT), bagging of model trees (BMT) and random forests of regression trees (RF). To compare the different regression methods, four datasets were used. The performance of the learned models was estimated by using 10-fold cross-validation and an additional hold-out test. For all datasets, better results were obtained by the nonlinear machine learning regression methods, which can explain more variance and yield lower error. However, none of the considered methods outperformed all other methods for all datasets. Therefore, we suggest testing several different methods before selecting the best one, e.g. for climate reconstruction"},{"@xml:lang":"sl","#text":"Različne študije so pokazale, da lahko z nelinearnimi metodami bolje opišemo (modeliramo) odnos med branikami in okoljem. V naši študiji smo primerjali (multiplo) linearno regresijo (MLR) in štiri nelinearne metode strojnega učenja: modelna drevesa (MT), ansambel bagging modelnih dreves (BMT), umetne nevronske mreže (ANN) in metodo naključnih gozdov (RF). Za primerjavo teh metod modeliranja smo uporabili štiri množice podatkov. Natančnost naučenih modelov smo ocenili z metodo 10-kratnega prečnega preverjanja (ang. 10-fold cross-validation) na naši množici in preverjanjem na dodatni testni množici. Na vseh množicah smo dobili boljše statistične kazalce za nelinearne metode s področja strojnega učenja, s katerimi lahko pojasnimo večji delež variance oz. dobimo manjšo napako. Nobena metoda se ni pokazala kot najboljša v vseh primerih, zato je smiselno predhodno primerjati več različnih metod in nato uporabiti najprimernejšo, npr. za rekonstrukcijo klime"}],"edm:type":"TEXT","dc:type":[{"@xml:lang":"sl","#text":"znanstveno časopisje"},{"@xml:lang":"en","#text":"journals"},{"@rdf:resource":"http://www.wikidata.org/entity/Q361785"}]},"ore:Aggregation":{"@rdf:about":"http://www.dlib.si/?URN=URN:NBN:SI:doc-DEY5BFG2","edm:aggregatedCHO":{"@rdf:resource":"URN:NBN:SI:doc-DEY5BFG2"},"edm:isShownBy":{"@rdf:resource":"http://www.dlib.si/stream/URN:NBN:SI:doc-DEY5BFG2/e8e3bb7e-0304-4cee-bdd5-b22f26676fe3/PDF"},"edm:rights":{"@rdf:resource":"http://rightsstatements.org/vocab/InC/1.0/"},"edm:provider":"Slovenian National E-content Aggregator","edm:intermediateProvider":{"@xml:lang":"en","#text":"National and University Library of Slovenia"},"edm:dataProvider":{"@xml:lang":"sl","#text":"Gozdarski inštitut Slovenije"},"edm:object":{"@rdf:resource":"http://www.dlib.si/streamdb/URN:NBN:SI:doc-DEY5BFG2/maxi/edm"},"edm:isShownAt":{"@rdf:resource":"http://www.dlib.si/details/URN:NBN:SI:doc-DEY5BFG2"}}}}