{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T10:01:36Z","timestamp":1760608896915},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319244648"},{"type":"electronic","value":"9783319244655"}],"license":[{"start":{"date-parts":[[2015,1,1]],"date-time":"2015-01-01T00:00:00Z","timestamp":1420070400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015]]},"DOI":"10.1007\/978-3-319-24465-5_3","type":"book-chapter","created":{"date-parts":[[2015,10,13]],"date-time":"2015-10-13T05:17:43Z","timestamp":1444713463000},"page":"25-36","source":"Crossref","is-referenced-by-count":3,"title":["Efficient Model Selection for Regularized Classification by Exploiting Unlabeled Data"],"prefix":"10.1007","author":[{"given":"Georgios","family":"Balikas","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ioannis","family":"Partalas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric","family":"Gaussier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rohit","family":"Babbar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Massih-Reza","family":"Amini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2015,11,22]]},"reference":[{"key":"3_CR1","unstructured":"Arlot, S., Lerasle, M.: Why V=5 is enough in V-fold cross-validation. ArXiv e-prints (2012)"},{"key":"3_CR2","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1214\/09-SS054","volume":"4","author":"S Arlot","year":"2010","unstructured":"Arlot, S., Celisse, A.: A survey of cross-validation procedures for model selection. Stat. Surv. 4, 40\u201379 (2010)","journal-title":"Stat. Surv."},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Babbar, R., Partalas, I., Gaussier, E., Amini, M.r.: Re-ranking approach to classification in large-scale power-law distributed category systems. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2014 (2014)","DOI":"10.1145\/2600428.2609509"},{"key":"3_CR4","doi-asserted-by":"crossref","unstructured":"Bella, A., Ferri, C., Hern\u00e1ndez-Orallo, J., Ram\u00edrez-Quintana, M.J.: Quantification via probability estimators. In: 2010 IEEE 10th International Conference on Data Mining, (ICDM), pp. 737\u2013742. IEEE (2010)","DOI":"10.1109\/ICDM.2010.75"},{"key":"3_CR5","first-page":"1089","volume":"5","author":"Y Bengio","year":"2004","unstructured":"Bengio, Y., Grandvalet, Y.: No unbiased estimator of the variance of k-fold cross-validation. J. Mach. Learn. Res. 5, 1089\u20131105 (2004)","journal-title":"J. Mach. Learn. Res."},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Blum, A., Kalai, A., Langford, J.: Beating the hold-out: bounds for k-fold and progressive cross-validation. In: Proceedings of the Twelfth Annual Conference on Computational Learning Theory, pp. 203\u2013208 (1999)","DOI":"10.1145\/307400.307439"},{"key":"3_CR7","unstructured":"Chapelle, O., Sch\u00f6lkopf, B., Zien, A. (eds.): Semi-Supervised Learning. MIT Press, Cambridge (2006). http:\/\/www.kyb.tuebingen.mpg.de\/ssl-book"},{"key":"3_CR8","unstructured":"Esuli, A., Sebastiani, F.: Optimizing text quantifiers for multivariate loss functions. Technical report 2013-TR-005, Istituto di Scienza e Tecnologie dellInformazione, Consiglio Nazionale delle Ricerche, Pisa, IT (2013)"},{"key":"3_CR9","first-page":"1871","volume":"9","author":"RE Fan","year":"2008","unstructured":"Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: A library for large linear classification. J. Mach. Learn. Res. 9, 1871\u20131874 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"3_CR10","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1007\/11564096_55","volume-title":"Machine Learning: ECML 2005","author":"G Forman","year":"2005","unstructured":"Forman, G.: Counting positives accurately despite inaccurate classification. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 564\u2013575. Springer, Heidelberg (2005)"},{"issue":"2","key":"3_CR11","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1007\/s10618-008-0097-y","volume":"17","author":"G Forman","year":"2008","unstructured":"Forman, G.: Quantifying counts and costs via classification. Data Min. Knowl. Discov. 17(2), 164\u2013206 (2008)","journal-title":"Data Min. Knowl. Discov."},{"key":"3_CR12","unstructured":"Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, IJCAI 1995 (1995)"},{"key":"3_CR13","volume-title":"Foundations of Machine Learning","author":"M Mohri","year":"2012","unstructured":"Mohri, M., Rostamizadeh, A., Talwalkar, A.: Foundations of Machine Learning. The MIT Press, Cambridge (2012)"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Partalas, I., Kosmopoulos, A., Baskiotis, N., Artieres, T., Paliouras, G., Gaussier, E., Androutsopoulos, I., Amini, M.R., Galinari, P.: Lshtc: A benchmark for large-scale text classification. CoRR abs\/1503.08581, March 2015","DOI":"10.1145\/2556195.2556208"},{"key":"3_CR15","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."}],"container-title":["Lecture Notes in Computer Science","Advances in Intelligent Data Analysis XIV"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-24465-5_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T01:00:05Z","timestamp":1653267605000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-24465-5_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015]]},"ISBN":["9783319244648","9783319244655"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-24465-5_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2015]]}}}