<Record><identifier xmlns="http://purl.org/dc/elements/1.1/">URN:NBN:SI:doc-V1SOW232</identifier><date>2021</date><creator>Gjoreski, Martin</creator><relation>documents/doc/V/URN_NBN_SI_doc-V1SOW232_001.pdf</relation><relation>documents/doc/V/URN_NBN_SI_doc-V1SOW232_001.txt</relation><format format_type="issue">1</format><format format_type="volume">45</format><format format_type="type">article</format><format format_type="extent">str. 169-170</format><identifier identifier_type="ISSN">0350-5596</identifier><identifier identifier_type="DOI">10.31449/inf.v45i1.3482</identifier><identifier identifier_type="COBISSID_HOST">70143491</identifier><identifier identifier_type="URN">URN:NBN:SI:doc-V1SOW232</identifier><language>eng</language><publisher>Slovensko društvo Informatika</publisher><source>Informatica (Ljubljana)</source><rights>BY</rights><subject language_type_id="slv">globoko učenje</subject><subject language_type_id="slv">mobilno spremljanje zdravja</subject><subject language_type_id="slv">strojno učenje</subject><subject language_type_id="slv">umetna inteligenca</subject><title>A method for combining classical and deep machine learning for mobile health and behavior monitoring</title></Record>