Klassifikation von Mustern

472 LITERATURVERZEICHNIS
[Och und Ney, 2002] Och, F.J., Ney, H. Discriminative training and maximum entropy models for statistical
machine translation. In Proc. 40th Annual Meeting of the Association for Computational
Linguistics. ACL, Philadelphia, USA, 2002.
[Olsen und Fukunaga, 1973] Olsen, D.R., Fukunaga, K. Representation of nonlinear data surfaces. IE-
EE Trans. on Computers, 22:915–922, 1973.
[Orchard, 1991] Orchard, M.T. A fast nearest-neighbor search algorithm. In Proc. Int. Conference on
Acoustics, Speech, and Signal Processing (ICASSP), S. 2297–2300. Toronto, Canada, 1991.
[Osborne et al., 2000] Osborne, M., Presnell, B., Turlach, B. On the LASSO and its dual. J. Computational
and Graphical Statistics, 9:319–337, 2000.
[Othman und Aboulnasr, 2003] Othman, H., Aboulnasr, T. A separable low complexity 2D HMM with
application to face recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence,
25:1229–1238, 2003.
[Pal und Bezdek, 1995] Pal, N., Bezdek, J.C. On cluster validity for the fuzzy c-means model. IEEE
Trans. on Fuzzy Systems, 3:370–379, 1995.
[Pal et al., 2005] Pal, N.R., Pal, K., Keller, J.M., Bezdek, J.C. A probabilistic fuzzy c-means clustering
algorithm. IEEE Trans. on Fuzzy Systems, 13:517–529, 2005.
[Panjwani und Healey, 1995] Panjwani, D.K., Healey, G. Markov random field texture models for unsupervised
segmentation of textured color images. IEEE Trans. on Pattern Analysis and Machine
Intelligence, 17:939–954, 1995.
[Papageorgiou, 1991] Papageorgiou, M. Optimierung. Oldenbourg, München, 1991.
[Park und Sandberg, 1991] Park, J., Sandberg, I.W. Universal approximation using radial-basis-function
networks. Neural Computation, 3:246–257, 1991.
[Park und Sandberg, 1993] Park, J., Sandberg, I.W. Approximation and radial-basis-function networks.
Neural Computation, 5:305–316, 1993.
[Parzen, 1962] Parzen, E. On estimation of a probability density and mode. Annals of Mathematical
Statistics, 33:1065–1076, 1962.
[Patrick, 1972] Patrick, E.A. Fundamentals of Pattern Recognition. Prentice Hall, Englewood Cliffs
N.J., 1972.
[Patrick und Costello, 1970] Patrick, E.A., Costello, J.P. On unsupervised estimation algorithms. IEEE
Trans. on Information Theory, 16:556–569, 1970.
[Patrick et al., 1970] Patrick, E.A., Costello, J.P., Monds, F.C. Decision directed estimation of a two
class decision boundary. IEEE Trans. on Computers, 19:197–205, 1970.
[Patrick und Hancock, 1966] Patrick, E.A., Hancock, J.P. Nonsupervised sequential classification and
recognition of patterns. IEEE Trans. on Information Theory, 12:362–372, 1966.
[Pavlidis et al., 2001] Pavlidis, P., Furey, T.S., Liberto, M., Haussler, D., Grundy, W.N. Promoter regionbased
classification of genes. In R.B. Altman, A.K. Dunker, L. Hunker, K. Lauderdale, T.E.
Klein, Hg., Proc. Pacific Symposium on Biocomputing 6 (http://psb.stanford.edu/psb-online), S.
151–164. (World Scientific Press, ISBN 981-02-4515-7), Mauna Lani, Hawaii, 2001.
[Payne und Meisel, 1977] Payne, H.J., Meisel, W.S. An algorithm for constructing optimal binary decision
trees. IEEE Trans. on Computers, 28:905–916, 1977.
[Pearl, 1988] Pearl, J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.
Morgan Kaufmann, San Mateo, CA, 1988.
[Pelillo und Refice, 1994] Pelillo, M., Refice, M. Learning compatibility coefficients for relaxation labeling.
IEEE Trans. on Pattern Analysis and Machine Intelligence, 16:933–945, 1994.
[Peng et al., 2003] Peng, J., Heisterkamp, D.R., Dai, H.K. LDA/SVM driven nearest neighbor classification.
IEEE Trans. on Image Processing, 14:940–942, 2003.
[Peng et al., 2004] Peng, J., Heisterkamp, D.R., Dai, H.K. Adaptive quasiconformal kernel nearest
neighbor classification. IEEE Trans. on Pattern Analysis and Machine Intelligence, 26:656–661,
2004.
[Piché, 1994] Piché, S.W. Steepest descent algorithms for neural network controllers and filters. IEEE