The Methodological Aspect of Development and Application Multivariate Classification G-Mode for Analyses Geochemical Trend

2014 ◽  
Vol 1 (1) ◽  
pp. 21-27
Author(s):  
A.I. Gavrishin ◽  
Keyword(s):  
Methodological Aspect ◽  
Multivariate Classification ◽  
Geochemical Trend

Explaining Knowledge

2017 ◽  
Keyword(s):  
State Of The Art ◽  
The State ◽  
Methodological Aspect ◽  
Gettier Problem ◽  
Contemporary Epistemology ◽  
The Gettier Problem

This is an edited collection of twenty-three new papers on the Gettier Problem and the issues connected with it. The set of authors includes many of the major figures in contemporary epistemology who have developed some of the well-known responses to the problem, and it also contains some younger epistemologists who bring new perspectives to the issues raised in the literature. Together, they cover the state of the art on virtually every epistemological and methodological aspect of the Gettier Problem. The volume also includes some skeptical voices according to which the Gettier Problem is not deeply problematic or some of the problems it raises are not genuine philosophical problems.

Multivariate classification of alcoholics by means of the MMPI.

1969 ◽  
Vol 74 (6) ◽  
pp. 661-669 ◽  
Author(s):  
Steven G. Goldstein ◽  
James D. Linden
Keyword(s):  
Multivariate Classification

scholarly journals High-pass filtering artifacts in multivariate classification of neural time series data

2021 ◽  
Vol 352 ◽  
pp. 109080
Author(s):  
Joram van Driel ◽  
Christian N.L. Olivers ◽  
Johannes J. Fahrenfort
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Multivariate Classification ◽  
High Pass

Impact of Laser-Induced Breakdown Spectroscopy data normalization on multivariate classification accuracy

2017 ◽  
Vol 32 (2) ◽  
pp. 277-288 ◽  
Author(s):  
P. Pořízka ◽  
J. Klus ◽  
A. Hrdlička ◽  
J. Vrábel ◽  
P. Škarková ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Laser Induced Breakdown Spectroscopy ◽  
Data Normalization ◽  
Spectroscopy Data ◽  
Multivariate Classification ◽  
Sample Matrix ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Normalization of data is significant and should be chosen according to the sample matrix under investigation.

Fast-HPLC Fingerprinting to Discriminate Olive Oil from Other Edible Vegetable Oils by Multivariate Classification Methods

2017 ◽  
Vol 100 (2) ◽  
pp. 345-350 ◽  
Author(s):  
Ana M Jiménez-Carvelo ◽  
Antonio González-Casado ◽  
Estefanía Pérez-Castaño ◽  
Luis Cuadros-Rodríguez
Keyword(s):  
Olive Oil ◽  
Vegetable Oils ◽  
Edible Oils ◽  
Support Vector ◽  
Classification Methods ◽  
Multivariate Classification ◽  
Chromatographic Fingerprint ◽  
K Nearest Neighbors ◽  
Pls Discriminant Analysis

Abstract A new analytical method for the differentiation of olive oil from other vegetable oils using reversed-phaseLC and applying chemometric techniques was developed. A 3 cm short column was used to obtain the chromatographic fingerprint of the methyl-transesterified fraction of each vegetable oil. The chromatographic analysis tookonly 4 min. The multivariate classification methods used were k-nearest neighbors, partial least-squares (PLS) discriminant analysis, one-class PLS, support vector machine classification, and soft independent modeling of class analogies. The discrimination of olive oil from other vegetable edible oils was evaluated by several classification quality metrics. Several strategies for the classification of the olive oil wereused: one input-class, two input-class, and pseudo two input-class.

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