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A non-parametric semi-supervised discretization method
Knowledge and Information Systems, 2009Semi-supervised classification methods aim to exploit labeled and unlabeled examples to train a predictive model. Most of these approaches make assumptions on the distribution of classes. This article first proposes a new semi-supervised discretization method, which adopts very low informative prior on data. This method discretizes the numerical domain
Alexis Bondu +2 more
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A non-parametric method to estimate the number of clusters
Computational Statistics & Data Analysis, 2014zbMATH Open Web Interface contents unavailable due to conflicting licenses.
André Fujita +2 more
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Parametric and Non-parametric Methods for Linear Extraction
2004This article presents two new approaches, one parametric and one non-parametric, to the linear grouping of image features. They are based on the Bayesian Hough Transform, which takes into account feature uncertainty. Our main contribution are two new ways to detect the most significant modes of the Hough Transform.
Benedicte Bascle +2 more
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Non-parametric and robust methods
1992The history of the statistical analysis of quantal response data is documented by Finney (1971, pp 38–42). The maximum-likelihood fitting of the probit model stems from the papers of Fisher (1935) and Bliss (1938). However, non-parametric approaches to estimating the ED 50 have a generally earlier origin, in papers such as Behrens (1929), Karber (1931)
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Non-parametric methods of system identification
Automatica, 1979The non-parametric identification of systems in terms of unparametrized representations such as the impulse response and frequency response is considered. Basic approaches are outlined in a retrospective setting as are the relationships between non-parametric and parametric identification models.
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Non-Parametric Spectral Methods
2015This chapter deals with obtaining a good estimate of the power spectrum of a random signal on the basis of a finite number of samples of a typical realization of the underlying random process—one among the infinite sequences that the process can generate when we measure it.
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A non-parametric method for feature selection
Seventh Symposium on Adaptive Processes, 1968A non-parametric feature selection technique is proposed. It is hoped that a finite number of classes is represented by some finite number of unknown probability structures which are distributed in a finite discrete measurement space. No assumptions of statistical independence between pattern measurements will be made.
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Non-Parametric Statistic Methods
In this chapter, we will explore various methods and statistical tests used in hypothesis validation and sample comparison. We will begin with an introduction to the formulation of scientific and statistical hypotheses and discuss the determination of the appropriate sample size.openaire +1 more source
Non-parametric methods in demand analysis
Economics Letters, 1982Abstract This note describes techniques developed by Afriat, Diewert, Varian, and others that allow empirical investigation of consumer demand data without imposing any maintained hypotheses concerning the parametric form of the underlying demand or utility functions.
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