scholarly journals Multivariate classification models

2021 ◽  
Author(s):  
Richard G. Brereton
Keyword(s):  
Classification Models ◽  
Multivariate Classification

scholarly journals Evaluation of Multivariate Classification Models for Analyzing NMR Metabolomics Data

2019 ◽  
Vol 18 (9) ◽  
pp. 3282-3294 ◽  
Author(s):  
Thao Vu ◽  
Parker Siemek ◽  
Fatema Bhinderwala ◽  
Yuhang Xu ◽  
Robert Powers
Keyword(s):  
Classification Models ◽  
Multivariate Classification ◽  
Metabolomics Data ◽  
Nmr Metabolomics

Impact of data reduction on multivariate classification models built on spectral data from bio-samples

2015 ◽  
Vol 30 (5) ◽  
pp. 1117-1127 ◽  
Author(s):  
Anders Larsson ◽  
Henrik Andersson ◽  
Lars Landström
Keyword(s):  
Spectral Data ◽  
Data Reduction ◽  
Image Data ◽  
Classification Models ◽  
Multivariate Classification

Multivariate classification models, both from 1D spectra and 2D image data (also with simulated shifts), were evaluated and compared.

Transfer of multivariate classification models applied to digital images and fluorescence spectroscopy data

2017 ◽  
Vol 133 ◽  
pp. 669-675 ◽  
Author(s):  
Karla Danielle Tavares Melo Milanez ◽  
Thiago César Araújo Nóbrega ◽  
Danielle Silva Nascimento ◽  
Matías Insausti ◽  
Márcio José Coelho Pontes
Keyword(s):  
Fluorescence Spectroscopy ◽  
Digital Images ◽  
Classification Models ◽  
Spectroscopy Data ◽  
Multivariate Classification

scholarly journals Intersession reproducibility of mass spectrometry profiles and its effect on accuracy of multivariate classification models

2007 ◽  
Vol 23 (22) ◽  
pp. 3065-3072 ◽  
Author(s):  
Richard Pelikan ◽  
William L. Bigbee ◽  
David Malehorn ◽  
James Lyons-Weiler ◽  
Milos Hauskrecht
Keyword(s):  
Mass Spectrometry ◽  
Classification Models ◽  
Multivariate Classification

Comparing the Speed and Accuracy of Multi-Label Classification Models

2015 ◽  
Vol 10 (8) ◽  
pp. 829
Author(s):  
Aswin Wibisurya ◽  
Ford Lumban Gaol ◽  
Kuncoro Wastuwibowo
Keyword(s):  
Classification Models ◽  
Speed And Accuracy

Effectiveness of Supervised Classification Models for Hate Speech on Twitter

2020 ◽  
Author(s):  
Kunal Srivastava ◽  
Ryan Tabrizi ◽  
Ayaan Rahim ◽  
Lauryn Nakamitsu
Keyword(s):  
Supervised Classification ◽  
Hate Speech ◽  
The Internet ◽  
Classification Models ◽  
Personal Attack ◽  
Political Beliefs ◽  
The Many ◽  
Primary Medium ◽  
Offensive Speech

<div> <div> <div> <p>Abstract </p> <p>The ceaseless connectivity imposed by the internet has made many vulnerable to offensive comments, be it their physical appearance, political beliefs, or religion. Some define hate speech as any kind of personal attack on one’s identity or beliefs. Of the many sites that grant the ability to spread such offensive speech, Twitter has arguably become the primary medium for individuals and groups to spread these hurtful comments. Such comments typically fail to be detected by Twitter’s anti-hate system and can linger online for hours before finally being taken down. Through sentiment analysis, this algorithm is able to distinguish hate speech effectively through the classification of sentiment. </p> </div> </div> </div>

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