{"type":"ScholarlyArticle","title":"A Framework for Long-Term Learning Systems","authors":[{"name":"Diana Benavides-Prado","slug":"diana-benavides-prado","given":"Diana","family":"Benavides-Prado","affiliation":"Department of Computer Science The University of Auckland"}],"abstract":"Increasing amounts of data have made the use of machine learning techniques much more widespread. A lot of research in machine learning has been dedicated to the design and application of effective and efficient algorithms to explain or predict facts. The development of intelligent machines that can learn over extended periods of time, and that improve their abilities as they execute more tasks, is still a pending contribution from computer science to the world. This weakness has been recognised for some decades, and an interest to solve it seems to be increasing, as demonstrated by recent leading work and broader discussions at main events in the field [Chen and Liu, 2015; Chen et al., 2016]. Our research is intended to help fill that gap.","doi":"10.24963/ijcai.2017/743","url":"https://ijcai.org/proceedings/2017/743","pdf":"https://ijcai.org/proceedings/2017/0743.pdf","markdown":"https://ijcai.org/proceedings/2017/743.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/743","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":743,"pages":{"first":5167,"last":5168},"keywords":["Artificial Intelligence: machine learning","Artificial Intelligence: computer science"],"videos":[],"references":[{"position":1,"raw":"[Chen et al., 2016] Zhiyuan Chen, Estevam Hruschka, and Bing Liu. Lifelong machine learning and computer read- ing the web. SIGKDD Tutorial, 2016.","title":"Lifelong machine learning and computer reading the web","authors":"Zhiyuan Chen, Estevam Hruschka, Bing Liu","year":"2016","venue":"SIGKDD Tutorial","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Fei et al., 2016] Geli Fei, Shuai Wang, and Bing Liu. Learn- ing cumulatively to become more knowledgeable. In SIGKDD, pages 1565-1574. ACM, 2016.","title":"Learning cumulatively to become more knowledgeable","authors":"Geli Fei, Shuai Wang, Bing Liu","year":"2016","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Kuzborskij and Orabona, 2013] Ilja Kuzborskij and Francesco Orabona. Stability and hypothesis transfer learning. In ICML, pages 942-950, 2013.","title":"Ilja Kuzborskij and Francesco Orabona. Stability and hypothesis transfer learning","authors":"Orabona Kuzborskij","year":"2013","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Lapin et al., 2014] Maksim Lapin, Matthias Hein, and Bernt Schiele. Learning using privileged information: SVM+ and weighted SVM. Neural Networks, 53:95-108, 2014.","title":"Learning using privileged information: SVM+ and weighted SVM","authors":"Maksim Lapin, Matthias Hein, Bernt Schiele","year":"2014","venue":"Neural Networks","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Pan and Yang, 2010] Sinno Jialin Pan and Qiang Yang. A survey on transfer learning. IEEE TKDE, 22(10):1345- 1359, 2010.","title":"A survey on transfer learning","authors":"Jialin Sinno, Qiang Pan, Yang","year":"2010","venue":"IEEE TKDE","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Ruvolo and Eaton, 2013] Paul Ruvolo and Eric Eaton. Ella: An efficient lifelong learning algorithm. ICML, 28:507- 515, 2013.","title":"Ella: An efficient lifelong learning algorithm","authors":"Eaton ; ] Ruvolo, Ruvolo Paul, Eric Eaton","year":"2013","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Silver et al., 2013] Daniel L Silver, Qiang Yang, and Liang- hao Li. Lifelong machine learning systems: Beyond learn- ing algorithms. In AAAI Spring Symposium: Lifelong Ma- chine Learning, volume 13, pages 49-53. Citeseer, 2013.","title":"Lifelong machine learning systems: Beyond learning algorithms","authors":"L Daniel, Qiang Silver, Lianghao Yang, Li","year":"2013","venue":"AAAI Spring Symposium: Lifelong Machine Learning","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Wang et al., 2014] Xuezhi Wang, Tzu-Kuo Huang, and Jeff G Schneider. Active transfer learning under model shift. In ICML, pages 1305-1313, 2014.","title":"Active transfer learning under model shift","authors":"Wang","year":"2014","venue":"ICML","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}