{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T19:24:56Z","timestamp":1781810696349,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":23,"publisher":"ACM","license":[{"start":{"date-parts":[[2015,8,10]],"date-time":"2015-08-10T00:00:00Z","timestamp":1439164800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"MEXT\/JSPS KAKENHI","award":["23700165,26106513"],"award-info":[{"award-number":["23700165,26106513"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2015,8,10]]},"DOI":"10.1145\/2783258.2783347","type":"proceedings-article","created":{"date-parts":[[2015,8,7]],"date-time":"2015-08-07T15:38:27Z","timestamp":1438961907000},"page":"885-894","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Quick Sensitivity Analysis for Incremental Data Modification and Its Application to Leave-one-out CV in Linear Classification Problems"],"prefix":"10.1145","author":[{"given":"Shota","family":"Okumura","sequence":"first","affiliation":[{"name":"Nagoya Institute of Technology, Nagoya, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshiki","family":"Suzuki","sequence":"additional","affiliation":[{"name":"Nagoya Institute of Technology, Nagoya, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ichiro","family":"Takeuchi","sequence":"additional","affiliation":[{"name":"Nagoya Institute of Technology, Nagoya, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2015,8,10]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Nonlinear Programming","author":"Bertsekas P D.","year":"1999","unstructured":"P D. Bertsekas . Nonlinear Programming . Athena Scientific , 1999 . P D. Bertsekas. Nonlinear Programming. Athena Scientific, 1999."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/993483"},{"key":"e_1_3_2_2_3_1","volume-title":"Advances in Neural Information Processing Systems","author":"Cauwenberghs G.","year":"2001","unstructured":"G. Cauwenberghs and T. Poggio . Incremental and decremental support vector machine learning . In Advances in Neural Information Processing Systems , 2001 . G. Cauwenberghs and T. Poggio. Incremental and decremental support vector machine learning. In Advances in Neural Information Processing Systems, 2001."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/347090.347165"},{"key":"e_1_3_2_2_6_1","volume-title":"Pacific Journal of Optimization","author":"El Ghaoui L.","year":"2012","unstructured":"L. El Ghaoui , V. Viallon , and T. Rabbani . Safe feature elimination in sparse supervised learning . Pacific Journal of Optimization , 2012 . L. El Ghaoui, V. Viallon, and T. Rabbani. Safe feature elimination in sparse supervised learning. Pacific Journal of Optimization, 2012."},{"key":"e_1_3_2_2_7_1","volume-title":"Advances in Neural Information Processing Systems","author":"Fine S.","year":"2001","unstructured":"S. Fine and K. Scheinberg . Incremental learning and selective sampling via parametric optimization framework for SVM . In Advances in Neural Information Processing Systems , 2001 . S. Fine and K. Scheinberg. Incremental learning and selective sampling via parametric optimization framework for SVM. In Advances in Neural Information Processing Systems, 2001."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-21606-5"},{"key":"e_1_3_2_2_9_1","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Jaakkola T.","year":"1999","unstructured":"T. Jaakkola and D. Haussler . Probabilistic kernel regression models . In International Conference on Artificial Intelligence and Statistics , 1999 . T. Jaakkola and D. Haussler. Probabilistic kernel regression models. In International Conference on Artificial Intelligence and Statistics, 1999."},{"key":"e_1_3_2_2_10_1","volume-title":"International Conference on Machine Learning","author":"Joachims T.","year":"2000","unstructured":"T. Joachims . Estimating the generalization performance of a SVM efficiently . In International Conference on Machine Learning , 2000 . T. Joachims. Estimating the generalization performance of a SVM efficiently. In International Conference on Machine Learning, 2000."},{"key":"e_1_3_2_2_11_1","volume-title":"Advances in Neural Information Processing Systems","author":"Karasuyama M.","year":"2009","unstructured":"M. Karasuyama and I. Takeuchi . Multiple incremental decremental learning of support vector machine . In Advances in Neural Information Processing Systems , 2009 . M. Karasuyama and I. Takeuchi. Multiple incremental decremental learning of support vector machine. In Advances in Neural Information Processing Systems, 2009."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.5555\/1248547.1248616"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2009.01.001"},{"key":"e_1_3_2_2_14_1","volume-title":"International Conference on Machine Learning","volume":"32","author":"Liu J.","year":"2014","unstructured":"J. Liu , Z. Zhao , J. Wang , and J. Ye . Safe Screening with Variational Inequalities and Its Application to Lasso . In International Conference on Machine Learning , volume 32 , 2014 . J. Liu, Z. Zhao, J. Wang, and J. Ye. Safe Screening with Variational Inequalities and Its Application to Lasso. In International Conference on Machine Learning, volume 32, 2014."},{"key":"e_1_3_2_2_15_1","volume-title":"International Conference on Machine Learning","author":"Ogawa K.","year":"2013","unstructured":"K. Ogawa , Y. Suzuki , and I. Takeuchi . Safe screening of non-support vectors in pathwise SVM computation . In International Conference on Machine Learning , 2013 . K. Ogawa, Y. Suzuki, and I. Takeuchi. Safe screening of non-support vectors in pathwise SVM computation. In International Conference on Machine Learning, 2013."},{"key":"e_1_3_2_2_16_1","volume-title":"Approximately optimal selection of regularization parameters in cross-validation for regularized classifiers. arXiv","author":"Shibagaki A.","year":"2015","unstructured":"A. Shibagaki , Y. Suzuki , and I. Takeuchi . Approximately optimal selection of regularization parameters in cross-validation for regularized classifiers. arXiv , 2015 . A. Shibagaki, Y. Suzuki, and I. Takeuchi. Approximately optimal selection of regularization parameters in cross-validation for regularized classifiers. arXiv, 2015."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2004.836201"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623661"},{"key":"e_1_3_2_2_19_1","volume-title":"The Nature of Statistical Learning Theory","author":"Vapnik V.","year":"1996","unstructured":"V. Vapnik . The Nature of Statistical Learning Theory . Springer , 1996 . V. Vapnik. The Nature of Statistical Learning Theory. Springer, 1996."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1162\/089976600300015042"},{"key":"e_1_3_2_2_21_1","volume-title":"Advances in Neural Information Processing Sysrtems","author":"Wang J.","year":"2014","unstructured":"J. Wang , J. Zhou , J. Liu , P. Wonka , and J. Ye . A Safe Screening Rule for Sparse Logistic Regression . In Advances in Neural Information Processing Sysrtems , 2014 . J. Wang, J. Zhou, J. Liu, P. Wonka, and J. Ye. A Safe Screening Rule for Sparse Logistic Regression. In Advances in Neural Information Processing Sysrtems, 2014."},{"key":"e_1_3_2_2_22_1","volume-title":"Advances in Neural Information Processing Sysrtems","author":"Xiang Z.","year":"2011","unstructured":"Z. Xiang , H. Xu , and P. Ramadge . Learning sparse representations of high dimensional data on large scale dictionaries . In Advances in Neural Information Processing Sysrtems , 2011 . Z. Xiang, H. Xu, and P. Ramadge. Learning sparse representations of high dimensional data on large scale dictionaries. In Advances in Neural Information Processing Sysrtems, 2011."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1162\/089976603321780326"}],"event":{"name":"KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","location":"Sydney NSW Australia","acronym":"KDD '15","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2783258.2783347","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/2783258.2783347","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T06:16:57Z","timestamp":1750227417000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2783258.2783347"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,8,10]]},"references-count":23,"alternative-id":["10.1145\/2783258.2783347","10.1145\/2783258"],"URL":"https:\/\/doi.org\/10.1145\/2783258.2783347","relation":{},"subject":[],"published":{"date-parts":[[2015,8,10]]},"assertion":[{"value":"2015-08-10","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}