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A wide range of methods including multivariate adaptive regression spline, support vector regression (SVR), radial basis function, random forest regression (RFR), and regression neural networks are adopted to recognize music emotions. Experimental results show that these regression algorithms have led to good regression effect for MER. The optimal R2 statistics and VA values are 29.3% and 62.5%, respectively, which are obtained by the RFR and SVR algorithms in the relief feature space.<\/p>","DOI":"10.4018\/ijcini.2016100104","type":"journal-article","created":{"date-parts":[[2016,11,29]],"date-time":"2016-11-29T16:22:49Z","timestamp":1480436569000},"page":"74-89","source":"Crossref","is-referenced-by-count":5,"title":["Dimensional Music Emotion Recognition by Machine Learning"],"prefix":"10.4018","volume":"10","author":[{"given":"Junjie","family":"Bai","sequence":"first","affiliation":[{"name":"School of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing, China & School of Instrument Science and Engineering, Southeast University, Nanjing, China"}]},{"given":"Lixiao","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing, China"}]},{"given":"Jun","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing, China"}]},{"given":"Jinliang","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing, China"}]},{"given":"Kan","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Fujian University of Technology, Fuzhou, China"}]},{"given":"Zuojin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing, China"}]},{"given":"Lu","family":"Liao","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing, China"}]},{"given":"Yingxu","family":"Wang","sequence":"additional","affiliation":[{"name":"International Institute of Cognitive Informatics and Cognitive Computing (ICIC),Laboratory for Computational Intelligence, Denotational Mathematics, and Software Science, Department of Electrical and Computer Engineering, Schulich School of Engineering and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada & Information Systems Lab, Stanford University, Stanford, CA, USA"}]}],"member":"2432","reference":[{"key":"IJCINI.2016100104-0","doi-asserted-by":"publisher","DOI":"10.1007\/s12555-012-9407-7"},{"key":"IJCINI.2016100104-1","first-page":"193","article-title":"Music emotion classification using double-layer support vector machines.","author":"Y. 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