{"type":"ScholarlyArticle","title":"Adaptive Support Vector Machine for Time-Varying Data Streams Using Martingale","authors":[{"name":"Shen-Shyang Ho","slug":"shen-shyang-ho","given":"Shen-Shyang","family":"Ho","affiliation":""},{"name":"Harry Wechsler","slug":"harry-wechsler","given":"Harry","family":"Wechsler","affiliation":""}],"abstract":"A martingale framework is proposed to enable support vector machine (SVM) to adapt to timevarying data streams. The adaptive SVM is a onepass incremental algorithm that (i) does not require a sliding window on the data stream, (ii) does not require monitoring the performance of the classifier as data points are streaming, and (iii) works well for high dimensional, multi-class data streams. Our experiments show that the novel adaptive SVM is effective at handling time-varying data streams simulated using both a synthetic dataset and a multiclass real dataset.","doi":null,"url":"https://ijcai.org/proceedings/2005/271","pdf":"https://ijcai.org/proceedings/2005/0271.pdf","markdown":"https://ijcai.org/proceedings/2005/271.md","bibtex":"https://ijcai.org/proceedings/2005/bibtex/271","volume":{"slug":"2005","title":"Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2005","url":"https://ijcai.org/proceedings/2005/","year":2005,"location":"Edinburgh, Scotland, UK","dates":"","editors":"","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":271,"pages":{"first":1606,"last":1606},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}