<Record><identifier xmlns="http://purl.org/dc/elements/1.1/">URN:NBN:SI:doc-W47O0FW9</identifier><date>2024</date><creator>Herakovič, Niko</creator><creator>Jankovič, Denis</creator><creator>Šimic, Marko</creator><relation>documents/doc/W/URN_NBN_SI_doc-W47O0FW9_001.pdf</relation><relation>documents/doc/W/URN_NBN_SI_doc-W47O0FW9_001.txt</relation><format format_type="volume">19</format><format format_type="type">article</format><format format_type="issue">nr. 1</format><format format_type="extent">str. 78–92</format><identifier identifier_type="DOI">10.14743/apem2024.1.494</identifier><identifier identifier_type="ISSN">1854-6250</identifier><identifier identifier_type="COBISSID_HOST">201874947</identifier><identifier identifier_type="URN">URN:NBN:SI:doc-W47O0FW9</identifier><language>eng</language><publisher publisher_location="Maribor">Fakulteta za strojništvo, Inštitut za proizvodno strojništvo</publisher><source>Advances in production engineering and management</source><rights>BY</rights><subject language_type_id="eng">artificial neural networks</subject><subject language_type_id="eng">decision trees</subject><subject language_type_id="eng">gaussian process regression</subject><subject language_type_id="slv">hidravlične stiskalnice</subject><subject language_type_id="eng">hydraulic presses</subject><subject language_type_id="eng">linear regression</subject><subject language_type_id="slv">linearna regresija</subject><subject language_type_id="eng">machine learning</subject><subject language_type_id="eng">metal forming</subject><subject language_type_id="slv">odločitvena drevesa</subject><subject language_type_id="slv">podporni vektorski stroji</subject><subject language_type_id="slv">preoblikovanje kovin</subject><subject language_type_id="slv">regresija Gaussovega procesa</subject><subject language_type_id="slv">strojno učenje</subject><subject language_type_id="eng">support vector machines</subject><subject language_type_id="slv">umetne nevronske mreže</subject><title>A comparative study of machine learning regression models for production systems condition monitoring</title></Record>