{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T07:34:00Z","timestamp":1762673640473},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2007,9,19]],"date-time":"2007-09-19T00:00:00Z","timestamp":1190160000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2007,10,3]]},"DOI":"10.1007\/s10994-007-5020-z","type":"journal-article","created":{"date-parts":[[2007,9,18]],"date-time":"2007-09-18T14:15:03Z","timestamp":1190124903000},"page":"35-53","source":"Crossref","is-referenced-by-count":20,"title":["Classifying under computational resource constraints: anytime classification using probabilistic estimators"],"prefix":"10.1007","volume":"69","author":[{"given":"Ying","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Geoff","family":"Webb","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kevin","family":"Korb","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai Ming","family":"Ting","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2007,9,19]]},"reference":[{"key":"5020_CR1","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","volume":"19","author":"H. Akaike","year":"1974","unstructured":"Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716\u2013723.","journal-title":"IEEE Transactions on Automatic Control"},{"key":"5020_CR2","volume-title":"Modern information retrieval","author":"R. Baeza-Yates","year":"1999","unstructured":"Baeza-Yates, R., & Ribeiro-Neto, B. (1999). Modern information retrieval. Reading: Addison\u2013Wesley."},{"key":"5020_CR3","unstructured":"Bernstein, D. S., Perkins, T. J., Zilberstein, S., & Finkelstein, L. (2002). Scheduling contract algorithms on multiple processors. In Proceedings of the 18th national conference on artificial intelligence and the 14th conference on innovative applications of artificial intelligence (pp. 702\u2013706)."},{"key":"5020_CR4","unstructured":"Blake, C., & Merz, C. J. (2004). UCI repository of machine learning databases. [Machine-readable data repository]. Department of Information and Computer Science, University of California, Irvine, CA, USA."},{"key":"5020_CR5","unstructured":"Breiman, L. (1996). Bias, variance and arcing classifiers (Technical report 460). Berkeley: Statistics Department, University of California."},{"issue":"6","key":"5020_CR6","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/5254.809570","volume":"14","author":"P. Chan","year":"1999","unstructured":"Chan, P., Fan, W., Prodromidis, A., & Stolfo, S. (1999). Distributed data mining in credit card fraud detection. IEEE Intelligent Systems, 14(6), 67\u201374.","journal-title":"IEEE Intelligent Systems"},{"key":"5020_CR7","unstructured":"DeCoste, D. (2002). Anytime interval-valued outputs for kernel machines: fast support vector machine classification via distance geometry. In Proceedings of the 19th international conference on machine learning (pp. 99\u2013106)."},{"key":"5020_CR8","first-page":"1","volume":"7","author":"J. Demsar","year":"2006","unstructured":"Demsar, J. (2006). Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7, 1\u201330.","journal-title":"Journal of Machine Learning Research"},{"issue":"1","key":"5020_CR9","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1023\/A:1009778005914","volume":"1","author":"J. H. Friedman","year":"1997","unstructured":"Friedman, J. H. (1997). On bias, variance, 0\/1-loss, and the curse-of-dimensionality. Data Mining and Knowledge Discovery, 1(1), 55\u201377.","journal-title":"Data Mining and Knowledge Discovery"},{"key":"5020_CR10","doi-asserted-by":"crossref","unstructured":"Grass, J., & Zilberstein, S. (1996). Anytime algorithm development tools. In M. Pittarelli (Ed.), SIGART Bulletin Special Issue on Anytime Algorithms and Deliberation Scheduling, 7(2), 20\u201327.","DOI":"10.1145\/242587.242592"},{"key":"5020_CR11","doi-asserted-by":"crossref","unstructured":"Grefenstette, J., & Ramsey, C. (1992). An approach to anytime learning. In Proceedings of the 9th international machine learning workshop.","DOI":"10.1016\/B978-1-55860-247-2.50029-2"},{"issue":"40","key":"5020_CR12","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1142\/S0218213002001052","volume":"11","author":"E. J. Keogh","year":"2002","unstructured":"Keogh, E. J., & Pazzani, M. J. (2002). Learning the structure of augmented Bayesian classifiers. International Journal on Artificial Intelligence Tools, 11(40), 587\u2013601.","journal-title":"International Journal on Artificial Intelligence Tools"},{"issue":"1\u20132","key":"5020_CR13","first-page":"273","volume":"97","author":"R. Kohavi","year":"1996","unstructured":"Kohavi, R., & John, G. H. (1996). Wrappers for feature subset selection. Artificial Intelligence, Special Issue on Relevance, 97(1\u20132), 273\u2013324.","journal-title":"Artificial Intelligence, Special Issue on Relevance"},{"key":"5020_CR14","unstructured":"Kohavi, R., & Wolpert, D. (1996). Bias plus variance decomposition for zero-one loss functions. In Proceedings of the 13th international conference on machine learning (pp. 275\u2013283)."},{"key":"5020_CR15","unstructured":"Koller, D., & Sahami, M. (1996). Toward optimal feature selection. In Proceedings of the 13th international conference on machine learning (pp. 284\u2013292)."},{"key":"5020_CR16","doi-asserted-by":"crossref","unstructured":"Kong, E. B., & Dietterich, T. G. (1995). Error-correcting output coding corrects bias and variance. In Proceedings of the 12th international conference on machine learning (pp. 313\u2013321).","DOI":"10.1016\/B978-1-55860-377-6.50046-3"},{"key":"5020_CR17","volume-title":"Bayesian artificial intelligence","author":"K. Korb","year":"2004","unstructured":"Korb, K., & Nicholson, A. (2004). Bayesian artificial intelligence. London: Chapman & Hall\/CRC."},{"key":"5020_CR18","doi-asserted-by":"crossref","unstructured":"Langley, P., & Sage, S. (1994). Induction of selective Bayesian classifiers. In Proceedings of the 10th annual conference on uncertainty in artificial intelligence.","DOI":"10.1016\/B978-1-55860-332-5.50055-9"},{"key":"5020_CR19","unstructured":"Langley, P., Iba, W., & Thompson, K. (1992). An analysis of Bayesian classifiers. In Proceedings of the 10th national conference on artificial intelligence (pp. 223\u2013228)."},{"key":"5020_CR20","doi-asserted-by":"crossref","unstructured":"Lewis, D. D. (1998). Naive Bayes at forty: the independence assumption in information retrieval. In Proceedings of the 10th European conference on machine learning (pp. 4\u201315).","DOI":"10.1007\/BFb0026666"},{"key":"5020_CR21","volume-title":"Machine learning","author":"T. M. Mitchell","year":"1997","unstructured":"Mitchell, T. M. (1997). Machine learning. New York: McGraw\u2013Hill."},{"key":"5020_CR22","unstructured":"Opitz, D. (1995). An anytime approach to confectionist theory refinement: refining the topologies of knowledge-based neural networks. Unpublished doctoral dissertation, Department of Computer Sciences, University of Wisconsin-Madison, USA."},{"issue":"3","key":"5020_CR23","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1145\/245108.245121","volume":"40","author":"P. Resnick","year":"1997","unstructured":"Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3), 56\u201358.","journal-title":"Communications of the ACM"},{"key":"5020_CR24","unstructured":"Sahami, M. (1996). Learning limited dependence Bayesian classifiers. In Proceedings of the 2nd international conference on knowledge discovery and data mining."},{"key":"5020_CR25","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1214\/aos\/1176344136","volume":"6","author":"G. Schwarz","year":"1978","unstructured":"Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461\u2013465.","journal-title":"Annals of Statistics"},{"key":"5020_CR26","unstructured":"Suzuki, J. (1996). Learning Bayesian belief networks based on the MDL principle: an efficient algorithm using the branch and bound technique. In Proceedings of the 13th international conference on machine learning (pp. 463\u2013470)."},{"key":"5020_CR27","unstructured":"Turney, P. (2000). Types of cost in inductive concept learning. In Workshop on cost-sensitive learning at ICML 2000 (pp. 15\u201321)."},{"issue":"2","key":"5020_CR28","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1023\/A:1007659514849","volume":"40","author":"G. I. Webb","year":"2000","unstructured":"Webb, G. I. (2000). Multiboosting: a technique for combining boosting and wagging. Machine Learning, 40(2), 159\u2013196.","journal-title":"Machine Learning"},{"issue":"1\u20132","key":"5020_CR29","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1023\/A:1011117102175","volume":"11","author":"G. I. Webb","year":"2001","unstructured":"Webb, G. I., Pazzani, M. J., & Billsus, D. (2001). Machine learning for user modeling. User Modeling and User-Adapted Interaction, 11(1\u20132), 19\u201329.","journal-title":"User Modeling and User-Adapted Interaction"},{"issue":"1","key":"5020_CR30","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s10994-005-4258-6","volume":"58","author":"G. I. Webb","year":"2005","unstructured":"Webb, G. I., Boughton, J., & Wang, Z. (2005). Not so naive Bayes: averaged one-dependence estimators. Machine Learning, 58(1), 5\u201324.","journal-title":"Machine Learning"},{"key":"5020_CR31","volume-title":"Data mining: practical machine learning tools and techniques with Java implementations","author":"I. H. Witten","year":"2005","unstructured":"Witten, I. H., & Frank, E. (2005). Data mining: practical machine learning tools and techniques with Java implementations (2nd ed.). Los Altos: Kaufmann.","edition":"2"},{"issue":"5","key":"5020_CR32","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1109\/69.806938","volume":"11","author":"X. Wu","year":"1999","unstructured":"Wu, X., & Urpani, D. (1999). Induction by attribute elimination. IEEE Transactions on Knowledge and Data Engineering, 11(5), 805\u2013812.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-007-5020-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10994-007-5020-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-007-5020-z","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,6,1]],"date-time":"2019-06-01T01:40:23Z","timestamp":1559353223000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10994-007-5020-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2007,9,19]]},"references-count":32,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2007,10,3]]}},"alternative-id":["5020"],"URL":"https:\/\/doi.org\/10.1007\/s10994-007-5020-z","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2007,9,19]]}}}