{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T21:42:17Z","timestamp":1725486137745},"publisher-location":"Berlin, Heidelberg","reference-count":10,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783540423591"},{"type":"electronic","value":"9783540445968"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2001]]},"DOI":"10.1007\/3-540-44596-x_6","type":"book-chapter","created":{"date-parts":[[2007,6,7]],"date-time":"2007-06-07T15:01:37Z","timestamp":1181228497000},"page":"62-72","source":"Crossref","is-referenced-by-count":0,"title":["Rule-Based Ensemble Solutions for Regression"],"prefix":"10.1007","author":[{"given":"Nitin","family":"Indurkhya","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sholom M.","family":"Weiss","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2001,7,26]]},"reference":[{"key":"6_CR1","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1023\/A:1007515423169","volume":"36","author":"E. Bauer","year":"1999","unstructured":"Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting and variants. Machine Learning, 36, 105\u2013139.","journal-title":"Machine Learning"},{"key":"6_CR2","first-page":"123","volume":"24","author":"L. Breiman","year":"1996","unstructured":"Breiman, L. (1996). Bagging predictors. Machine Learning, 24, 123\u2013140.","journal-title":"Machine Learning"},{"key":"6_CR3","volume-title":"Classification and regression trees","author":"L. Breiman","year":"1984","unstructured":"Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees. Monterrey, CA.: Wadsworth."},{"key":"6_CR4","unstructured":"Cohen, W., & Singer, Y. (1999). A simple, fast, and effective rule learner. Proceedings of Annual Conference of American Association for Artificial Intelligence (pp. 335\u2013342)."},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Dougherty, J., Kohavi, R., & Sahami, M. (1995). Supervised and unsupervised discretization of continuous features. Proceedings of the 12th Int\u2019l Conference on Machine Learning (pp. 194\u2013202).","DOI":"10.1016\/B978-1-55860-377-6.50032-3"},{"key":"6_CR6","unstructured":"Friedman, J., Hastie, T., & Tibshirani, R. (1998). Additive logistic regression: A statistical view of boosting (Technical Report). Stanford University Statistics Department. http:\/\/www.stat-stanford.edu\/~tibs ."},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Hartigan, J., & Wong, M. (1979). A k-means clustering algorithm, ALGORITHM AS 136. Applied Statistics, 28.","DOI":"10.2307\/2346830"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Schapire, R., Freund, Y., Bartlett, P., & Lee, W. (1998). Boosting the margin: A new explanation for the effectiveness of voting methods. Proceedings of the Fourteenth Int\u2019l Conference on Machine Learning (pp. 322\u2013330). Morgan Kaufmann.","DOI":"10.1214\/aos\/1024691352"},{"key":"6_CR9","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1613\/jair.199","volume":"3","author":"S. Weiss","year":"1995","unstructured":"Weiss, S., & Indurkhya, N. (1995). Rule-based machine learning methods for functional prediction. Journal of Artificial Intelligence Research, 3, 383\u2013403.","journal-title":"Journal of Artificial Intelligence Research"},{"key":"6_CR10","unstructured":"Weiss, S., & Indurkhya, N. (2000). Lightweight rule induction. Proceedings of the Seventeenth International Conference on Machine Learning (pp. 1135\u20131142)."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Data Mining in Pattern Recognition"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/3-540-44596-X_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,4,28]],"date-time":"2019-04-28T16:28:08Z","timestamp":1556468888000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/3-540-44596-X_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2001]]},"ISBN":["9783540423591","9783540445968"],"references-count":10,"URL":"https:\/\/doi.org\/10.1007\/3-540-44596-x_6","relation":{},"ISSN":["0302-9743"],"issn-type":[{"type":"print","value":"0302-9743"}],"subject":[],"published":{"date-parts":[[2001]]}}}