{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/an-efficient-training-algorithm-for-kernel","title":"An Efficient Training Algorithm for Kernel Survival Support Vector Machines","arxiv_id":"1611.07054","date":"2016-11-21","proceeding":null,"authors":["Sebastian Pölsterl","Nassir Navab","Amin Katouzian"],"abstract":"Survival analysis is a fundamental tool in medical research to identify\npredictors of adverse events and develop systems for clinical decision support.\nIn order to leverage large amounts of patient data, efficient optimisation\nroutines are paramount. We propose an efficient training algorithm for the\nkernel survival support vector machine (SSVM). We directly optimise the primal\nobjective function and employ truncated Newton optimisation and order statistic\ntrees to significantly lower computational costs compared to previous training\nalgorithms, which require $O(n^4)$ space and $O(p n^6)$ time for datasets with\n$n$ samples and $p$ features. Our results demonstrate that our proposed\noptimisation scheme allows analysing data of a much larger scale with no loss\nin prediction performance. Experiments on synthetic and 5 real-world datasets\nshow that our technique outperforms existing kernel SSVM formulations if the\namount of right censoring is high ($\\geq85\\%$), and performs comparably\notherwise.","url_abs":"http://arxiv.org/abs/1611.07054v1","url_pdf":"http://arxiv.org/pdf/1611.07054v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"an-efficient-training-algorithm-for-kernel","repo_url":"https://github.com/tum-camp/survival-support-vector-machine","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"an-efficient-training-algorithm-for-kernel","repo_url":"https://github.com/sebp/scikit-survival","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"survival-analysis","task_name":"Survival Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}