Klassifikation von Mustern

476 LITERATURVERZEICHNIS
[Schölkopf, 1997] Schölkopf, B. Support Vector Learning. GMD-Bericht Nr. 287. Oldenbourg, München,
1997.
[Schölkopf et al., 2000] Schölkopf, B., Smola, A.J., Williamson, R.C., Bartlett, P.I. New support vector
algorithms. Neural Computation, 12:1207–1245, 2000.
[Schroeter und Bigün, 1995] Schroeter, P., Bigün, J. Hierarchical image segmentation by multidimensional
clustering and orientation-adaptive boundary refinement. Pattern Recognition,
28:695–709, 1995.
[Schukat-Talamazzini, 1995] Schukat-Talamazzini, E.G. Automatische Spracherkennung. Vieweg,
Wiesbaden, 1995.
[Schürmann, 1971] Schürmann, J. Über systematisch konstruierte nichtlineare Klassifikatoren für die
Handblockschrift-Erkennung. Elektron. Rechenanlagen, 13:250–260, 1971.
[Schürmann, 1977] Schürmann, J. Polynomklassifikatoren für die Zeichenerkennung. R. Oldenbourg,
München, 1977.
[Schürmann, 1978] Schürmann, J. A multifont word recognition system for postal address reading.
IEEE Trans. on Computers, 27:721–732, 1978.
[Schürmann, 1982] Schürmann, J. Reading machines. In Proc. Int. Conference on Pattern Recognition
(ICPR), S. 1031–1044. München, Germany, 1982.
[Schürmann und Becker, 1978] Schürmann, J., Becker, D. Spracherkennung mit Quadratmittel-
Polynomklassifikatoren. Elektron. Rechenanlagen, 20:15–23, 65–71, 1978.
[Schürmann und Krause, 1974] Schürmann, J., Krause, P. Vergleich zweier quadratischer Klassifikatoren
am gleichen Datenmaterial. Elektron. Rechenanlagen, 16:132–142, 1974.
[Schwarz et al., 1968] Schwarz, H., Rutishauser, H., Stiefel, E. Numerik symmetrischer Matrizen. B.G.
Teubner, Stuttgart, 1968.
[Scott, 1992] Scott, D.W. Multivariate Density Estimation. J. Wiley, New York, 1992.
[Scudder, 1965] Scudder, H.J. Adaptive communication receivers. IEEE Trans. on Information Theory,
11:167–174, 1965.
[Sebestyen, 1962] Sebestyen, G. Decision Making Processes in Pattern Recognition. MacMillan, New
York, 1962.
[Sebestyen und Edie, 1966] Sebestyen, G., Edie, J. An algorithm for nonparametric pattern recognition.
IEEE Trans. on Electronic Computers, 15:908–915, 1966.
[Serratosa und Sanfeliu, 2006] Serratosa, F., Sanfeliu, A. Signatures versus histograms: Definitions,
distances, and algorithms. Pattern Recognition, 39:921–934, 2006.
[Sethi und Chatterjee, 1977] Sethi, I.K., Chatterjee, B. Efficient decision tree design for discrete variable
pattern recognition problems. Pattern Recognition, 9:197–206, 1977.
[Shapiro und Haralick, 1979] Shapiro, L.G., Haralick, R.M. Decomposition of two-dimensional shapes
by graph-theoretic clustering. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1:10–
20, 1979.
[Shawe-Taylor und Cristianini, 2004] Shawe-Taylor, J., Cristianini, N. Kernel Methods for Pattern Analysis.
Cambridge University Press, Cambridge, 2004.
[Shepard, 1962] Shepard, R.N. The analysis of proximities: Multidimensional scaling with an unknown
distance function I and II. Psychometrika, 27:125–140 and 219–246, 1962.
[Silverman, 1978] Silverman, B.W. Choosing the window width when estimating a density. Biometrica,
65:1–11, 1978.
[Silverman, 1985] Silverman, B.W. Density Estimation for Statistics and Data Analysis. Chapman and
Hall, New York, USA, 1985.
[Silverman, 1986] Silverman, B.W. Density Estimation for Statistics and Data Analysis. Chapman and
Hall, London, 1986.
[Simons et al., 1997] Simons, M., Ney, H., Martin, S.C. Distant bigram language modeling using maximum
entropy. In Proc. Int. Conference on Acoustics, Speech, and Signal Processing (ICASSP), S.
Vol. II, 787–790. (IEEE Computer Society Press, Los Alamitos, CA), Munich, Germany, 1997.