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<p>Die Seite wurde neu angelegt: „ == Reference == W. B. Langdon and B. F. Buxton: Genetic Programming for Combining Classifiers. Proceedings of the Genetic and Evolutionary Computation Confer…“</p> <p><b>Neue Seite</b></p><div><br /> <br /> == Reference ==<br /> W. B. Langdon and B. F. Buxton: Genetic Programming for Combining Classifiers. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pp. 66-73, Morgan Kaufmann, 7-11 July 2001. <br /> <br /> == DOI ==<br /> <br /> == Abstract ==<br /> Genetic programming (GP) can automat-<br /> ically fuse given classifiers to produce a<br /> combined classifier whose Receiver Operat-<br /> ing Characteristics (ROC) are better than<br /> [Scott et al., 1998b]’s “Maximum Realisable<br /> Receiver Operating Characteristics” (MR-<br /> ROC). I.e. better than their convex hull.<br /> This is demonstrated on artificial, medical<br /> and satellite image processing bench marks.<br /> <br /> == Extended Abstract ==<br /> <br /> == Bibtex == <br /> <br /> == Used References ==<br /> [Freund and Schapire, 1996] Y. Freund and R. E.<br /> Schapire. Experiments with a new boosting algo-<br /> rithm. In Machine Learning: Proc. 13th Interna-<br /> tional Conference, pp 148–156. Morgan Kaufmann.<br /> <br /> [Jacobs et al., 1991] R. A. Jacobs, M. I. Jordon, S. J.<br /> Nowlan, and G. E. Hinton. Adaptive mixtures of<br /> local experts. Neural Computation, 3:79–87, 1991.<br /> <br /> Kohavi and Sommerfield, 1996] R. Kohavi and D.<br /> Sommerfield. MLC++: Machine learning library<br /> in C++. Technical report, http://www.sgi.com/Technology/mlc/util/util.ps.<br /> <br /> [Langdon and Buxton, 2001] Evolving receiver oper-<br /> ating characteristics for data fusion. In J. F. Miller <br /> et al., eds., EuroGP’2001, LNCS 2038, pp 87–96,<br /> Springer-Verlag.<br /> <br /> [Langdon, 1998] W. B. Langdon. Data Structures and<br /> Genetic Programming. Kluwer.<br /> <br /> [Langdon, 2000] W. B. Langdon. Size fair and homol-<br /> ogous tree genetic programming crossovers. Genetic<br /> Programming &amp; Evolvable Machines, 1(1/2):95–119.<br /> <br /> [Mitchell, 1997] T. M. Mitchell. Machine Learning.<br /> McGraw-Hill, 1997.<br /> <br /> [Ripley, 1996] B. D. Ripley. Pattern Recognition and<br /> Neural Networks. Cambridge University Press.<br /> <br /> [Scott et al., 1998a] M. J. J. Scott, M. Niranjan, and<br /> R. W. Prager. Parcel: feature subset selection in<br /> variable cost domains. Technical Report CUED/F-<br /> INFENG/TR.323, Cambridge University, UK.<br /> <br /> [Scott et al., 1998b] Realisable classifiers: Improving<br /> operating performance on variable cost problems.<br /> In P. H. Lewis and M. S. Nixon, eds., Ninth British<br /> Machine Vision Conference, pages 304–315,<br /> <br /> [Soule, 1999] T. Soule. Voting teams: A cooperative<br /> approach to non-typical problems using genetic pro-<br /> gramming. In W. Banzhaf et al., eds., GECCO,<br /> pages 916–922. Morgan Kaufmann.<br /> <br /> [Swets et al., 2000] J. A. Swets, R. M. Dawes, and J.<br /> Monahan. Better decisions through science. Scien-<br /> tific American, pages 70–75, October.<br /> <br /> [Yusoff et al., 1998] Combining multiple experts for<br /> classifying shot changes in video sequences. In IEEE<br /> Int. Conf. on Multimedia Computing and Systems.<br /> <br /> <br /> == Links ==<br /> === Full Text === <br /> http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL_gecco2001_roc.pdf<br /> <br /> [[intern file]]<br /> <br /> === Sonstige Links ===</div>

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