{"type":"ScholarlyArticle","title":"Fast Cross-Validation for Incremental Learning","authors":[{"name":"Pooria Joulani","slug":"pooria-joulani","given":"Pooria","family":"Joulani","affiliation":""},{"name":"András György","slug":"andras-gyorgy","given":"András","family":"György","affiliation":""},{"name":"Csaba Szepesvári","slug":"csaba-szepesvari","given":"Csaba","family":"Szepesvári","affiliation":""}],"abstract":"Cross-validation (CV) is one of the main tools for performance estimation and parameter tuning in machine learning. The general recipe for computing CV estimate is to run a learning algorithm separately for each CV fold, a computationally expensive process. In this paper, we propose a new approach to reduce the computational burden of CV-based performance estimation. As opposed to all previous attempts, which are specific to a particular learning model or problem domain, we propose a general method applicable to a large class of incremental learning algorithms, which are uniquely fitted to big data problems. In particular, our method applies to a wide range of supervised and unsupervised learning tasks with different performance criteria, as long as the base learning algorithm is incremental. We show that the running time of the algorithm scales logarithmically, rather than linearly, in the number of CV folds. Furthermore, the algorithm has favorable properties for parallel and distributed implementation. Experiments with state-of-the-art incremental learning algorithms confirm the practicality of the proposed method.","doi":null,"url":"https://ijcai.org/proceedings/2015/506","pdf":"https://ijcai.org/proceedings/2015/0506.pdf","markdown":"https://ijcai.org/proceedings/2015/506.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/506","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Special Track on Machine Learning","track":"Special Track on Machine Learning","number":506,"pages":{"first":3597,"last":3603},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}