{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T22:53:12Z","timestamp":1725490392157},"publisher-location":"Berlin, Heidelberg","reference-count":15,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783540442097"},{"type":"electronic","value":"9783540457831"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2002]]},"DOI":"10.1007\/3-540-45783-6_60","type":"book-chapter","created":{"date-parts":[[2007,8,27]],"date-time":"2007-08-27T16:30:58Z","timestamp":1188232258000},"page":"498-506","source":"Crossref","is-referenced-by-count":12,"title":["Maximum Entropy and Gaussian Models for Image Object Recognition"],"prefix":"10.1007","author":[{"given":"Daniel","family":"Keysers","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Franz Josef","family":"Och","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hermann","family":"Ney","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2002,10,10]]},"reference":[{"issue":"1","key":"60_CR1","first-page":"39","volume":"22","author":"A.L. Berger","year":"1996","unstructured":"A.L. Berger, S.A. Della Pietra, V.J. Della Pietra: A Maximum Entropy Approach to Natural Language Processing. Computational Linguistics, 22(1):39\u201372, March 1996.","journal-title":"Computational Linguistics"},{"issue":"3","key":"60_CR2","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1023\/A:1011242314266","volume":"14","author":"J. Dahmen","year":"2001","unstructured":"J. Dahmen, D. Keysers, H. Ney, M.O. G\u00fcld: Statistical Image Object Recognition using Mixture Densities. J. Mathematical Imaging and Vision, 14(3):285\u2013296, May 2001.","journal-title":"J. Mathematical Imaging and Vision"},{"key":"60_CR3","doi-asserted-by":"crossref","unstructured":"J. Dahmen, R. Schl\u00fcter, H. Ney: Discriminative Training of Gaussian Mixture Densities for Image Object Recognition. In 21. DAGM Symposium Mustererkennung, Bonn, Germany, pp. 205\u2013212, September 1999.","DOI":"10.1007\/978-3-642-60243-6_24"},{"issue":"5","key":"60_CR4","doi-asserted-by":"publisher","first-page":"1470","DOI":"10.1214\/aoms\/1177692379","volume":"43","author":"J.N. Darroch","year":"1972","unstructured":"J.N. Darroch, D. Ratcliff: Generalized Iterative Scaling for Log-Linear Models. Annals of Mathematical Statistics, 43(5):1470\u20131480, 1972.","journal-title":"Annals of Mathematical Statistics"},{"key":"60_CR5","first-page":"470","volume-title":"Advances in Neural Information Processing Systems","author":"T. Jaakkola","year":"2000","unstructured":"T. Jaakkola, M. Meila, T. Jebara: Maximum Entropy Discrimination. In Advances in Neural Information Processing Systems 12, MIT Press, Cambridge, MA, pp. 470\u2013476, 2000."},{"issue":"9","key":"60_CR6","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1109\/PROC.1982.12425","volume":"70","author":"E.T. Jaynes","year":"1982","unstructured":"E.T. Jaynes: On the Rationale of Maximum Entropy Models. Proc. of the IEEE, 70(9):939\u2013952, September 1982.","journal-title":"Proc. of the IEEE"},{"key":"60_CR7","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/ICPR.2000.906014","volume":"2","author":"D. Keysers","year":"2000","unstructured":"D. Keysers, J. Dahmen, T. Theiner, H. Ney: Experiments with an Extended Tangent Distance. In Proc. 15th IEEE Int. Conf. on Pattern Recognition, volume 2, Barcelona, Spain, pp. 38\u201342, September 2000.","journal-title":"Proc. 15th IEEE Int. Conf. on Pattern Recognition"},{"key":"60_CR8","unstructured":"K. Nigam, J. Lafferty, A. McCallum: Using Maximum Entropy for Text Classification. In IJCAI-99 Workshop on Machine Learning for Information Filtering, Stockholm, Sweden, pp. 61\u201367, August 1999."},{"key":"60_CR9","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/978-1-4613-1367-0_3","volume-title":"Automatic Speech and Speaker Recognition","author":"Y. Normandin","year":"1996","unstructured":"Y. Normandin: Maximum Mutual Information Estimation of Hidden Markov Models. In C.H. Lee, F.K. Soong, K.K. Paliwal (Eds.): Automatic Speech and Speaker Recognition, Kluwer Academic Publishers, Norwell, MA, pp. 57\u201381, 1996."},{"key":"60_CR10","volume-title":"Numerical Recipes in C","author":"W.H. Press","year":"1992","unstructured":"W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery: Numerical Recipes in C. Cambridge University Press, Cambridge, second edition, 1992.","edition":"second edition"},{"key":"60_CR11","volume-title":"Support Vector Learning","author":"B. Sch\u00f6lkopf","year":"1997","unstructured":"B. Sch\u00f6lkopf: Support Vector Learning. Oldenbourg Verlag, Munich, 1997."},{"key":"60_CR12","first-page":"640","volume":"10","author":"B. Sch\u00f6lkopf","year":"1998","unstructured":"B. Sch\u00f6lkopf, P. Simard, A. Smola, V. Vapnik: Prior Knowledge in Support Vector Kernels. In Advances in Neural Information Processing Systems 10. MIT Press, pp. 640\u2013646, 1998.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"60_CR13","series-title":"Lect Notes Comput Sci","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1007\/3-540-49430-8_13","volume-title":"Neural Networks: Tricks of the Trade","author":"P. Simard","year":"1998","unstructured":"P. Simard, Y. Le Cun, J. Denker, B. Victorri: Transformation Invariance in Pattern Recognition \u2014 Tangent Distance and Tangent Propagation. In G. Orr, K.R. M\u00fcller (Eds.): Neural Networks: Tricks of the Trade, volume 1524 of Lecture Notes in Computer Science, Springer, Heidelberg, pp. 239\u2013274, 1998."},{"key":"60_CR14","first-page":"50","volume-title":"Advances in Neural Information Processing Systems","author":"P. Simard","year":"1993","unstructured":"P. Simard, Y. Le Cun, J. Denker: Efficient Pattern Recognition Using a New Transformation Distance. In Advances in Neural Information Processing Systems 5, Morgan Kaufmann, San Mateo, CA, pp. 50\u201358, 1993."},{"key":"60_CR15","first-page":"332","volume":"12","author":"M.E. Tipping","year":"2000","unstructured":"M.E. Tipping: The Relevance Vector Machine. In Advances in Neural Information Processing Systems 12. MIT Press, pp. 332\u2013388, 2000.","journal-title":"Advances in Neural Information Processing Systems"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/3-540-45783-6_60","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,2]],"date-time":"2019-05-02T16:40:08Z","timestamp":1556815208000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/3-540-45783-6_60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2002]]},"ISBN":["9783540442097","9783540457831"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/3-540-45783-6_60","relation":{},"ISSN":["0302-9743"],"issn-type":[{"type":"print","value":"0302-9743"}],"subject":[],"published":{"date-parts":[[2002]]}}}