{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T14:32:29Z","timestamp":1774621949521,"version":"3.50.1"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"2-3","license":[{"start":{"date-parts":[[2007,1,30]],"date-time":"2007-01-30T00:00:00Z","timestamp":1170115200000},"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":[[2007,3]]},"DOI":"10.1007\/s10994-007-0717-6","type":"journal-article","created":{"date-parts":[[2007,2,2]],"date-time":"2007-02-02T13:03:37Z","timestamp":1170421417000},"page":"209-241","source":"Crossref","is-referenced-by-count":23,"title":["Optimal dyadic decision trees"],"prefix":"10.1007","volume":"66","author":[{"given":"G.","family":"Blanchard","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C.","family":"Sch\u00e4fer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Y.","family":"Rozenholc","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K.-R.","family":"M\u00fcller","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2007,1,30]]},"reference":[{"key":"717_CR1","first-page":"1259","volume":"3","author":"G. M. Adelson-Velskii","year":"1962","unstructured":"Adelson-Velskii, G. M., & Landis, E. M. (1962). An algorithm for the organization of information. Soviet Math. Doclady, 3, 1259\u20131263.","journal-title":"Soviet Math. Doclady"},{"key":"717_CR2","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s004400050210","volume":"113","author":"A. Barron","year":"1999","unstructured":"Barron, A., Birg\u00e9, L., & Massart, P. (1999). Risk bounds for model selection via penalization. Probability Theory and Related Fields, 113, 301\u2013413.","journal-title":"Probability Theory and Related Fields"},{"key":"717_CR3","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1214\/aos\/1176348252","volume":"19","author":"A. Barron","year":"1991","unstructured":"Barron, A., & Sheu, C. (1991). Approximation of density functions by sequences of exponential families. Annals of Statistics, 19, 1347\u20131369.","journal-title":"Annals of Statistics"},{"issue":"4","key":"717_CR4","doi-asserted-by":"crossref","first-page":"1497","DOI":"10.1214\/009053605000000282","volume":"33","author":"P. Bartlett","year":"2005","unstructured":"Bartlett, P., Bousquet, O., & Mendelson, S. (2005). Local Rademacher complexities. Annals of Statistics, 33(4), 1497\u20131537.","journal-title":"Annals of Statistics"},{"key":"717_CR5","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1162\/089976604322860712","volume":"16","author":"G. Blanchard","year":"2004","unstructured":"Blanchard, G. (2004). Different paradigms for choosing sequential reweighting algorithms. Neural Computation, 16, 811\u2013836.","journal-title":"Neural Computation"},{"key":"717_CR6","unstructured":"Blanchard, G., Bousquet, O., & Massart, P. (2004). Statistical performance of support Vector Machines. Submitted manuscript."},{"key":"717_CR7","doi-asserted-by":"crossref","unstructured":"Blanchard, G., Sch\u00e4fer, C., & Rozenholc, Y. (2004). Oracle bounds and exact algorithm for dyadic classification trees. In J. Shawe-Taylor & Y. Singer (Eds.), Proceedings of the 17th Conference on Learning Theory (COLT 2004), number 3210 in lectures notes in artificial intelligence (pp. 378\u2013392). Springer.","DOI":"10.1007\/978-3-540-27819-1_26"},{"key":"717_CR8","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L. Breiman","year":"2001","unstructured":"Breiman, L. (2001). Random forests. Machine Learning, 45, 5\u201332.","journal-title":"Machine Learning"},{"key":"717_CR9","unstructured":"Breiman, L., Friedman, J., Olshen, J., & Stone, C. (1984). Classification and Regression Trees. Wadsworth."},{"issue":"8","key":"717_CR10","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1016\/S0764-4442(00)00250-0","volume":"330","author":"G. Castellan","year":"2000","unstructured":"Castellan, G. (2000). Histograms selection with an Akaike type criterion. C. R. Acad. Sci., Paris, S\u00e9r. I, Math., 330(8), 729\u2013732.","journal-title":"C. R. Acad. Sci., Paris, S\u00e9r. I, Math."},{"key":"717_CR11","doi-asserted-by":"crossref","unstructured":"Cover, T. M., & Thomas, J. A. (1991). Elements of information theory. Wiley series in telecommunications. J. Wiley.","DOI":"10.1002\/0471200611"},{"key":"717_CR12","doi-asserted-by":"crossref","unstructured":"Devroye, L., Gy\u00f6rfi, L., & Lugosi, G. (1996). A Probabilistic Theory of Pattern Recognition. Number 31 in Applications of Mathematics. New York: Springer.","DOI":"10.1007\/978-1-4612-0711-5"},{"key":"717_CR13","doi-asserted-by":"crossref","first-page":"1870","DOI":"10.1214\/aos\/1069362377","volume":"25","author":"D. Donoho","year":"1997","unstructured":"Donoho, D. (1997). Cart and best-ortho-basis: A connection. Annals of Statistics, 25, 1870\u20131911.","journal-title":"Annals of Statistics"},{"issue":"2","key":"717_CR14","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1109\/TIT.2004.840903","volume":"51","author":"S. Gey","year":"2005","unstructured":"Gey, S., & N\u00e9d\u00e9lec, E. (2005). Model selection for CART regression trees. IEEE Transactions on Information Theory, 51(2), 658\u2013670.","journal-title":"IEEE Transactions on Information Theory"},{"key":"717_CR15","doi-asserted-by":"crossref","unstructured":"Gy\u00f6rfi, L., Kohler, M., & Krzyzak, A. (2002). A distribution-free theory of nonparametric regression. Springer series in statistics. Springer.","DOI":"10.1007\/b97848"},{"key":"717_CR16","unstructured":"Klemel\u00e4, J. (2003). Multivariate histograms with data-dependent partitions. Technical report, Institut f\u00fcr angewandte Mathematik, Universit\u00e4t Heidelberg."},{"issue":"2","key":"717_CR17","doi-asserted-by":"crossref","first-page":"245","DOI":"10.5802\/afst.961","volume":"9","author":"P. Massart","year":"2000","unstructured":"Massart, P. (2000). Some applications of concentration inequalities in statistics. Ann. Fac. Sci. Toulouse Math., 9(2), 245\u2013303.","journal-title":"Ann. Fac. Sci. Toulouse Math."},{"key":"717_CR18","doi-asserted-by":"crossref","unstructured":"Mika, S., R\u00e4tsch, G., Weston, J., Sch\u00f6lkopf, B., & M\u00fcller, K.-R. (1999). Fisher discriminant analysis with kernels. In Y.-H. Hu, J. Larsen, E. Wilson & S. Douglas (Eds.), Neural networks for signal processing IX (pp. 41\u201348). IEEE.","DOI":"10.1109\/NNSP.1999.788121"},{"key":"717_CR19","volume-title":"C4.5: Programs for Machine Learning","author":"J. R. Quinlan","year":"1993","unstructured":"Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo."},{"issue":"3","key":"717_CR20","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1023\/A:1007618119488","volume":"42","author":"G. R\u00e4tsch","year":"2001","unstructured":"R\u00e4tsch, G., Onoda, T., & M\u00fcller, K.-R. (2001). Soft margins for AdaBoost. Machine Learning, 42(3), 287\u2013320. also NeuroCOLT Technical Report NC-TR-1998-021.","journal-title":"Machine Learning"},{"key":"717_CR21","unstructured":"Scott, C., & Nowak, R. (2004). Near-minimax optimal classification with dyadic classification trees. In S. Thrun, L. Saul & B. Sch\u00f6lkopf (Eds.), Advances in neural information processing systems 16. Cambridge, MA: MIT Press."},{"issue":"4","key":"717_CR22","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1109\/TIT.2006.871056","volume":"52","author":"C. Scott","year":"2006","unstructured":"Scott, C., & Nowak, R. (2006). Minimax optimal classification with dyadic decision trees. IEEE Transactions on Information Theory, 52(4), 1335\u20131353.","journal-title":"IEEE Transactions on Information Theory"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-007-0717-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10994-007-0717-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-007-0717-6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,31]],"date-time":"2019-05-31T21:40:22Z","timestamp":1559338822000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10994-007-0717-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2007,1,30]]},"references-count":22,"journal-issue":{"issue":"2-3","published-print":{"date-parts":[[2007,3]]}},"alternative-id":["717"],"URL":"https:\/\/doi.org\/10.1007\/s10994-007-0717-6","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2007,1,30]]}}}