Results 251 to 260 of about 624,838 (295)
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Nonparametric Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1983A nonparametric method of discriminant analysis is proposed. It is based on nonparametric extensions of commonly used scatter matrices. Two advantages result from the use of the proposed nonparametric scatter matrices. First, they are generally of full rank. This provides the ability to specify the number of extracted features desired.
Keinosuke Fukunaga, James M. Mantock
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Nonparametric Background Generation
18th International Conference on Pattern Recognition (ICPR'06), 2006A novel background generation method based on nonparametric background model is presented for background subtraction. We introduce a new model, named as effect components description (ECD), to model the variation of the background, by which we can relate the best estimate of the background to the modes (local maxima) of the underlying distribution ...
Yazhou Liu +4 more
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On Nonparametric Estimation of a Nonparametric Autoregressive Conditionally Heteroscedastic Process
Afrika Statistika, 2022Since the studies of Engel (1982) and Bollerslev (1986), the ARCH and GARCH processes have been used extensively to model volatile series. However, Pagan and Schwert (1990) have shown the limits of these choices. This deficiency is overcome by the NonParametric AutoRegressive Conditionally Heteroscedastic (NPARCH) processes.
Kouassi, Ben Célestin +2 more
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984
A nonparametric data reduction technique is proposed. Its goal is to select samples that are ``representative'' of the entire data set. The technique is iterative and is based on the use of a criterion function and nearest neighbor density estimates. Experiments are presented to demonstrate the algorithm.
Keinosuke Fukunaga, James M. Mantock
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A nonparametric data reduction technique is proposed. Its goal is to select samples that are ``representative'' of the entire data set. The technique is iterative and is based on the use of a criterion function and nearest neighbor density estimates. Experiments are presented to demonstrate the algorithm.
Keinosuke Fukunaga, James M. Mantock
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IEEE Transactions on Information Theory, 1993
Summary: Burg's standard maximum entropy method and the resulting autoregressive model has been widely applied for spectrum estimation and prediction. A generalization of the maximum entropy formalism in a nonparametric setting is presented, and the class of the resulting solutions is identified to be a class of Markov processes.
Politis, Dimitris Nicolas +1 more
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Summary: Burg's standard maximum entropy method and the resulting autoregressive model has been widely applied for spectrum estimation and prediction. A generalization of the maximum entropy formalism in a nonparametric setting is presented, and the class of the resulting solutions is identified to be a class of Markov processes.
Politis, Dimitris Nicolas +1 more
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Nonparametric NMR Spectroscopy
Journal of Magnetic Resonance, 2001The parametric (or model-based) approach to NMR spectroscopy suffers from two general problems: it is sensitive to modeling errors and requires knowledge of the number of resonances present in the compound(s) under analysis. The nonparametric approach has neither of these drawbacks and it may also be computationally simpler than the parametric approach.
P, Stoica, T, Sundin
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Decentralized nonparametric detectors
IEEE Signal Processing Letters, 1997A system formed by N identical nonparametric detectors and a fusion center is studied. The detectors transmit their decisions to the fusion center. The detectors are based either on the signs (sign test) or on the signs and the ranks of the observations (Wilcoxon test).
Emad K. Al-Hussaini, Yousry A. El-Far
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Combinatorics and Nonparametric mathematics
Annals of Combinatorics, 1997Nonparametric statistics, toric varieties, matroids – a more disparate trio of mathematical subjects can hardly be imagined, and yet, they share a basic idea. The idea is to replace a numerical or continuous quantity in an existing “classical” subject by a discrete combinatorial quality.
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