{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-bag-to-class-divergence-approach-to","title":"A bag-to-class divergence approach to multiple-instance learning","arxiv_id":"1803.02782","date":"2018-03-07","proceeding":null,"authors":["Kajsa Møllersen","Jon Yngve Hardeberg","Fred Godtliebsen"],"abstract":"In multi-instance (MI) learning, each object (bag) consists of multiple\nfeature vectors (instances), and is most commonly regarded as a set of points\nin a multidimensional space. A different viewpoint is that the instances are\nrealisations of random vectors with corresponding probability distribution, and\nthat a bag is the distribution, not the realisations. In MI classification,\neach bag in the training set has a class label, but the instances are\nunlabelled. By introducing the probability distribution space to bag-level\nclassification problems, dissimilarities between probability distributions\n(divergences) can be applied. The bag-to-bag Kullback-Leibler information is\nasymptotically the best classifier, but the typical sparseness of MI training\nsets is an obstacle. We introduce bag-to-class divergence to MI learning,\nemphasising the hierarchical nature of the random vectors that makes bags from\nthe same class different. We propose two properties for bag-to-class\ndivergences, and an additional property for sparse training sets.","url_abs":"http://arxiv.org/abs/1803.02782v2","url_pdf":"http://arxiv.org/pdf/1803.02782v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-bag-to-class-divergence-approach-to","repo_url":"https://github.com/kajsam/Bag-to-class-divergence","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multiple-instance-learning","task_name":"Multiple Instance Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}