20.500.12556/RUL-153064 Positive unlabeled learning with tensor networks Positive unlabeled learning is a binary classification problem with positive and unlabeled data. It is common in domains where negative labels are costly or impossible to obtain, e.g., medicine and personalized advertising. Most approaches to positive unlabeled learning apply to specific data types (e.g., images, categorical data) and can not generate new positive and negative samples. This work introduces a feature-space distance-based tensor network approach to the positive unlabeled learning problem. The presented method is not domain specific and significantly improves the state-of-the-art results on the MNIST image and 15 categorical/mixed datasets. The trained tensor network model is also a generative model and enables the generation of new positive and negative instances. positive unlabeled learning tensor networks matrix product states pozitivno neoznačeno učenje tenzorske mreže matrično produktni nastavek true false true Angleški jezik Slovenski jezik Članek v reviji 2023-12-15 14:10:38 2023-12-15 14:10:38 2023-12-16 03:49:06 0000-00-00 00:00:00 2023 0 0 11 str. art. 126556 Vol. 552 Oct. 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004 0925-2312 10.1016/j.neucom.2023.126556 177078019 1-s2.0-S0925231223006793-main.pdf 1-s2.0-S0925231223006793-main.pdf 1 847FB0466DD1A59F005FDD7C792AF758 f13d15522ac00541b0ee9642c2f35ea610a6229cb0c7526dae3321268cac0be6 97b1cab2-9b4b-11ee-bebf-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=178865 https://www.sciencedirect.com/science/article/pii/S0925231223006793 0 57a52a28-9b4b-11ee-bebf-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=178864 Fakulteta za računalništvo in informatiko 0 0 0