{"type":"ScholarlyArticle","title":"An Efficient Classifier Based on Hierarchical Mixing Linear Support Vector Machines","authors":[{"name":"Di Wang","slug":"di-wang","given":"Di","family":"Wang","affiliation":""},{"name":"Xiaoqin Zhang","slug":"xiaoqin-zhang","given":"Xiaoqin","family":"Zhang","affiliation":""},{"name":"Mingyu Fan","slug":"mingyu-fan","given":"Mingyu","family":"Fan","affiliation":""},{"name":"Xiuzi Ye","slug":"xiuzi-ye","given":"Xiuzi","family":"Ye","affiliation":""}],"abstract":"Support vector machines (SVMs) play a very dominant role in data classification due to their good generalization performance. However, they suffer from the high computational complexity in the classification phase when there are a considerable number of support vectors (SVs). Then it is desirable to design efficient algorithms in the classification phase to deal with the datasets of real-time pattern recognition systems. To this end, we propose a novel classifier called HMLSVMs (Hierarchical Mixing Linear Support Vector Machines) in this paper, which has a hierarchical structure with a mixing linear SVMs classifier at each node and predicts the label of a sample using only a few hyperplanes. We also give a generalization error bound for the class of locally linear SVMs (LLSVMs) based on the Rademacher theory, which ensures that overfitting can be effectively avoided. Experimental evaluations shows, while maintaining a comparable classification performance to kernel SVMs (KSVMs), the proposed classifier achieves the high efficiency in the classification stage.","doi":null,"url":"https://ijcai.org/proceedings/2015/547","pdf":"https://ijcai.org/proceedings/2015/0547.pdf","markdown":"https://ijcai.org/proceedings/2015/547.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/547","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":547,"pages":{"first":3897,"last":3902},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}