{"type":"ScholarlyArticle","title":"A Simple Additive Re-weighting Strategy for Improving Margins","authors":[{"name":"Fabio Aiolli","slug":"fabio-aiolli","given":"Fabio","family":"Aiolli","affiliation":""},{"name":"Alessandro Sperduti","slug":"alessandro-sperduti","given":"Alessandro","family":"Sperduti","affiliation":""}],"abstract":"We present a sample re-weighting scheme inspired by recent results in margin theory. The basic idea is to add to the training set replicas of samples which are not classified with a sufficient margin. We prove the convergence of the input distribution obtained in this way. As study case, we consider an instance of the scheme involving a 1-NN classifier implementing a Vector Quantization algorithm that accommodates tangent distance models. The tangent distance models created in this way have shown a significant improvement in generalization power with respect to the standard tangent models. Moreover, the obtained models were able to outperform state of the art algorithms, such as SVM.","doi":null,"url":"https://ijcai.org/proceedings/2001/127","pdf":"https://ijcai.org/proceedings/2001/0127.pdf","markdown":"https://ijcai.org/proceedings/2001/127.md","bibtex":"https://ijcai.org/proceedings/2001/bibtex/127","volume":{"slug":"2001","title":"Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2001","url":"https://ijcai.org/proceedings/2001/","year":2001,"location":"Seattle, Washington, USA","dates":"","editors":"","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":127,"pages":{"first":927,"last":933},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}