<Record><identifier xmlns="http://purl.org/dc/elements/1.1/">URN:NBN:SI:doc-LSBYV3EM</identifier><date>2025</date><creator>Shi, Feng</creator><relation>documents/doc/L/URN_NBN_SI_doc-LSBYV3EM_001.pdf</relation><relation>documents/doc/L/URN_NBN_SI_doc-LSBYV3EM_001.txt</relation><format format_type="volume">49</format><format format_type="issue">6</format><format format_type="type">article</format><format format_type="extent">str. 191-204</format><identifier identifier_type="DOI">10.31449/inf.v49i6.6809</identifier><identifier identifier_type="ISSN">1854-3871</identifier><identifier identifier_type="COBISSID">236116227</identifier><identifier identifier_type="URN">URN:NBN:SI:doc-LSBYV3EM</identifier><language>eng</language><publisher publisher_location="Ljubljana">Informatika</publisher><source>Informatica (Ljubljana)</source><rights>BY</rights><subject language_type_id="slv">algoritem Ada Boost</subject><subject language_type_id="slv">analiza gibanja</subject><subject language_type_id="slv">metoda podpornih vektorjev</subject><subject language_type_id="slv">namizni tenis</subject><subject language_type_id="slv">umetna inteligenca</subject><title>A motion capture framework for table tennis using optimized SVM and AdaBoost algorithms</title></Record>