Potential methods in pattern recognition

Coomans, Danny; Massart, D.L.; Broeckaert, I.; Tassin, Aude

doi:10.1016/s0003-2670(01)83196-2

Cited by 55 publications

(4 citation statements)

References 14 publications

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“…Potential function methods (PFM) constitute a family of non-parametric probabilistic techniques derived from the work of Coomans and Broeckaert [45]. The first class-modelling version is due to Forina and co-workers [46].…”

Section: Pfmmentioning

confidence: 99%

Class-modelling in food analytical chemistry: Development, sampling, optimisation and validation issues – A tutorial

Oliveri

2017

Analytica Chimica Acta

186

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Section: Pfmmentioning

confidence: 99%

Class-modelling in food analytical chemistry: Development, sampling, optimisation and validation issues – A tutorial

Oliveri

2017

Analytica Chimica Acta

186

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“…The smaller the residuals, the better the fit of the model, i.e., the more of the variance in the data is explained. The number A of statistically significant components (product terms) is determined by cross validation (20). PC based models are sensitive to the (…”

Section: Theorymentioning

confidence: 99%

“…We emphasize that closure is a general problem that affects the data and hence any multivariate data analytic method applied to it, e.g., factor analysis (17), KNN (18), LDA (19), and ALLOC (20). It also affects ordinary variable by variable plots.…”

Section: Theorymentioning

confidence: 99%

Minimizing effects of closure on analytical data

Sjoedin¹

1984

Anal. Chem.

120

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flowmeter should perform well for most polymers encountered in GPC. Since the flowmeter precision decreases as the molecular weight of the polymer increases, and since GPC becomes more difficult at high molecular weights, problems with the flowmeter should occur only in extreme cases.The analysis presented here assumes that the cell delay constant, Kc (cf. eq 1), does not change with concentration or the power law constant. Experimentally, Kc does change but it is not correlated with concentration for the data obtained. Our experience is that the reproducibility of Kc for a given GPC system and flowmeter is such that overall flow measurement precision is about 0.2% (6).The flowmeter employed in this research is the subject of a pending patent and licensed to Molytek, Inc., for commercial use. ACKNOWLEDGMENTWe thank Gene Rose for many useful discussions and assistance with the rheological measurements and Tom Chamberlin for helpful comments.LITERATURE CITED(1) Miller, T. E.; Small, H.

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“…Potential function methods (PFM) constitute a family of non-parametric probabilistic techniques derived from the work of Coomans et al 2 The first class-modelling version was derived by Forina et al 3 PFM estimate a probability density distribution of a class of interest as a sum of contributions from each single sample of the class in a training set (Figure 1…”

mentioning

confidence: 99%

Potential function methods: Efficient probabilistic approaches to model complex data distributions

Oliveri

2017

NIR news

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Potential function methods are a family of non-parametric probabilistic techniques that estimate a probability density distribution of a class of interest as a sum of contributions from each single sample of the class. The resulting estimated overall probability distribution can be sectioned at different confidence levels, with isoprobability contours representing the boundaries of the class space at each confidence level. Boundaries of the class spaces can be very complex, capable of effectively describing non-normal and non-uniform sample distributions.

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Potential methods in pattern recognition

Cited by 55 publications

References 14 publications

Class-modelling in food analytical chemistry: Development, sampling, optimisation and validation issues – A tutorial

Class-modelling in food analytical chemistry: Development, sampling, optimisation and validation issues – A tutorial

Minimizing effects of closure on analytical data

Potential function methods: Efficient probabilistic approaches to model complex data distributions

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