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MIL, where training data are prepared in the form of labeled bags rather than labeled instances, is a variant of supervised learning. This paper presents a novel MIL algorithm for an extreme learning machine called MI-ELM. A radial basis kernel extreme learning machine is adapted to approach the MIL problem using Hausdorff distance to measure the distance between the bags. The clusters in the hidden layer are composed of bags that are randomly generated. Because we do not need to tune the parameters for the hidden layer, MI-ELM can learn very fast. The experimental results on classifications and multiple-instance regression data sets demonstrate that the MI-ELM is useful and efficient as compared to the state-of-the-art algorithms.<\/jats:p>","DOI":"10.1515\/jisys-2015-0011","type":"journal-article","created":{"date-parts":[[2016,3,17]],"date-time":"2016-03-17T04:48:05Z","timestamp":1458190085000},"page":"185-195","source":"Crossref","is-referenced-by-count":6,"title":["Multiple-Instance Learning via an RBF Kernel-Based Extreme Learning Machine"],"prefix":"10.1515","volume":"26","author":[{"given":"Jie","family":"Wang","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4944-1507","authenticated-orcid":false,"given":"Liangjian","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China"}]},{"given":"Xin","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China"}]}],"member":"374","published-online":{"date-parts":[[2016,3,17]]},"reference":[{"key":"2025120523260708833_j_jisys-2015-0011_ref_001_w2aab3b7c61b1b6b1ab2b1b1Aa","doi-asserted-by":"crossref","unstructured":"S. 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