{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T04:36:33Z","timestamp":1780634193276,"version":"3.54.1"},"publisher-location":"Berlin, Heidelberg","reference-count":10,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"value":"9783540228813","type":"print"},{"value":"9783540286516","type":"electronic"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2004]]},"DOI":"10.1007\/978-3-540-28651-6_19","type":"book-chapter","created":{"date-parts":[[2010,9,16]],"date-time":"2010-09-16T20:04:40Z","timestamp":1284667480000},"page":"132-141","source":"Crossref","is-referenced-by-count":4,"title":["Synergy of Logistic Regression and Support Vector Machine in Multiple-Class Classification"],"prefix":"10.1007","author":[{"given":"Yuan-chin Ivar","family":"Chang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sung-Chiang","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","reference":[{"key":"19_CR1","unstructured":"Allwein, E., Schapire, R., Singer, Y.: Reducing Multiclass to Binary: A unifying Approach for Margin Classifiers. Journal of Machine Learning Research, 113\u2013141 (2000)"},{"key":"19_CR2","doi-asserted-by":"publisher","first-page":"11","DOI":"10.2307\/2336391","volume":"71","author":"C.B. Begg","year":"1984","unstructured":"Begg, C.B., Gray, R.: Calculation of polychotomous logistic regression parameters using individualized regression. Biometrika\u00a071, 11\u201318 (1984)","journal-title":"Biometrika"},{"key":"19_CR3","unstructured":"Blake, C.L., Merz, C.J.: UCI repository of Machine Learning databases (1998)"},{"key":"19_CR4","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511801389","volume-title":"An Introduction to Support Vector Machines and Other Kernel-based Learning Methods","author":"N. Cristianini","year":"2000","unstructured":"Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, Cambridge (2000)"},{"key":"19_CR5","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1007\/978-1-4612-2952-0_15","volume-title":"Advances in GLIM and Statistical Modelling","author":"D. Firth","year":"1992","unstructured":"Firth, D.: Bias reduction, the Jefferys prior and GLIM. In: Fahemeir, L., Francis, R.G., Tutz, G. (eds.) Advances in GLIM and Statistical Modelling, pp. 91\u2013100. Springer, New York (1992)"},{"key":"19_CR6","doi-asserted-by":"publisher","first-page":"2109","DOI":"10.1002\/sim.1047","volume":"21","author":"Heinze","year":"2002","unstructured":"Heinze, Schemper: A solution to the problem of separation in logistic regression. Statist. Med.\u00a021, 2109\u20132149 (2002)","journal-title":"Statist. Med."},{"key":"19_CR7","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4899-3242-6","volume-title":"Generalized Linear Models","author":"P. McCullagh","year":"1989","unstructured":"McCullagh, P.: Generalized Linear Models, 2nd edn. Chapman and Hall, New York (1989)","edition":"2"},{"key":"19_CR8","unstructured":"Rifkin, R., Klautau, A.: In defence of one-vs-all classification. Journal of Machine Learning Research, 101\u2013141 (2004)"},{"key":"19_CR9","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/4175.001.0001","volume-title":"Learning with Kernels:Support Vector machines, Regularization, Optimization, and Beyond","author":"B. Sch\u00f6lkopf","year":"2001","unstructured":"Sch\u00f6lkopf, B., Smola, A.J.: Learning with Kernels:Support Vector machines, Regularization, Optimization, and Beyond. MIT press, Cambridge (2001)"},{"key":"19_CR10","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-2440-0","volume-title":"The Nature of Statistical Learning Theory","author":"V. Vapnik","year":"1995","unstructured":"Vapnik, V.: The Nature of Statistical Learning Theory. Springer, NY (1995)"}],"container-title":["Lecture Notes in Computer Science","Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2004"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-540-28651-6_19.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T21:53:54Z","timestamp":1740520434000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-540-28651-6_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2004]]},"ISBN":["9783540228813","9783540286516"],"references-count":10,"URL":"https:\/\/doi.org\/10.1007\/978-3-540-28651-6_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2004]]}}}