Klassifikation von Mustern

LITERATURVERZEICHNIS 473
Trans. on Neural Networks, 5:198–212, 1994.
[Plagianakos et al., 2002] Plagianakos, V.P., Magoulas, G.D., Vrahatis, M.N. Deterministic nonmonotone
strategies for efficient training of multilayer perceptrons. IEEE Trans. on Neural Networks,
13:1268–1284, 2002.
[Platt, 1998] Platt, J.C. Fast training of support vector machines using sequential minimal optimization.
In B. Schölkopf, C. Burges, A. Smola, Hg., Advances in Kernel Methods: Support Vector
Machines. MIT Press, Cambridge, Mass., 1998.
[Platt, 1999] Platt, J.C. Probabilistic outputs for support vector machines and comparisons to regularized
likelihoods. In A. Smola, P. Bartlett, B. and Schölkopf, Hg., Advances in Large Margin Classifiers.
The MIT Press, Cambridge, Mass., 1999.
[Poli et al., 1991] Poli, R., Cagnoni, S., Livi, R., Coppini, G., Valli, G. A neural network expert system
for diagnosing and treating hypertension. IEEE Computer, 24(3):64–71, 1991.
[Pösl, 1999] Pösl, J. Erscheinungsbasierte statistische Objekterkennung. Berichte aus der Informatik.
Shaker Verlag, Aachen, Germany, 1999.
[Postaire und Vasseur, 1981] Postaire, J.G., Vasseur, C.P.A. An approximate solution to normal mixture
identification with application to unsupervised pattern classification. IEEE Trans. on Pattern
Analysis and Machine Intelligence, 3:163–179, 1981.
[Prim, 1957] Prim, R.C. Shortest connection networks and some generalizations. Bell Systems Technical
Journal, 36:1389–1401, 1957.
[Principe et al., 2000] Principe, J., Xu, D., Fisher, J. Information theoretic learning. In S. Haykin, Hg.,
Unsupervised Adaptive Filtering. J. Wiley, New York, 2000.
[Pujari, 2001] Pujari, A.K. Data Mining Techniques. University Press, Nancy, 2001.
[Purnell und Botha, 2002] Purnell, D.W., Botha, E.C. Improved generalization of MCE parameter estimation
with application to speech recognition. IEEE Trans. on Speech and Audio Processing,
10:232–239, 2002.
[Rabiner und Juang, 1993] Rabiner, L., Juang, B.-H. Fundamentals of Speech Recognition. Prentice
Hall Signal Processing Series. Prentice Hall, Englewood Cliffs, N.J., 1993.
[Rabiner, 1988] Rabiner, L.R. Mathematical foundations of hidden Markov models. In H. Niemann,
M. Lang, G. Sagerer, Hg., Recent Advances in Speech Understanding and Dialog Systems, Bd. 46
von NATO ASI Series F, S. 183–205. Springer, Berlin, 1988.
[Rabiner et al., 1979] Rabiner, L.R., Levinson, S.E., Rosenberg, A.E., Wilpon, J.G. Speakerindependent
recognition of isolated words using clustering techniques. IEEE Trans. on Acoustics,
Speech, and Signal Processing, 27:336–349, 1979.
[Rabiner und Schmidt, 1980] Rabiner, L.R., Schmidt, G.E. Application of dynamic time warping to
connected digit recognition. IEEE Trans. on Acoustics, Speech, and Signal Processing, 28:377–
388, 1980.
[Ramasubramanian und Paliwal, 1990] Ramasubramanian, V., Paliwal, K.K. An efficient
approximation-elimination algorithm for fast nearest neighbour search based on a spherical
distance coordinate formulation. In L. Torres-Urgell, M.A. Lagunas-Hernandez, Hg., Signal
Processing V: Theories and Applications (Proc. EUSIPCO), S. 1323–1326. (North Holland,
Amsterdam), Barcelona, Spain, 1990.
[Ramasubramanian und Paliwal, 2000] Ramasubramanian, V., Paliwal, K.K. Fast nearest-neighbor
search algorithms based on approximation-elimination search. Pattern Recognition, 33:1497–
1510, 2000.
[Rao, 1973] Rao, C.R., Hg. Linear Statistical Inference and its Applications. J. Wiley, New York, 1973.
[Raphael, 2001] Raphael, C. Coarse-to-fine dynamic programming. IEEE Trans. on Pattern Analysis
and Machine Intelligence, 23:1379–1390, 2001.
[Rätsch et al., 2002] Rätsch, G., Mika, S., Schölkopf, B., Müller, K.-R. Constructing boosting algorithms
from SVMs: An application to one-class classification. IEEE Trans. on Neural Networks,
24:1184–1199, 2002.