scholarly journals Multivariate classification models

2021 ◽  
Author(s):  
Richard G. Brereton
2019 ◽  
Vol 18 (9) ◽  
pp. 3282-3294 ◽  
Author(s):  
Thao Vu ◽  
Parker Siemek ◽  
Fatema Bhinderwala ◽  
Yuhang Xu ◽  
Robert Powers

2015 ◽  
Vol 30 (5) ◽  
pp. 1117-1127 ◽  
Author(s):  
Anders Larsson ◽  
Henrik Andersson ◽  
Lars Landström

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


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

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

2015 ◽  
Vol 10 (8) ◽  
pp. 829
Author(s):  
Aswin Wibisurya ◽  
Ford Lumban Gaol ◽  
Kuncoro Wastuwibowo

2020 ◽  
Author(s):  
Kunal Srivastava ◽  
Ryan Tabrizi ◽  
Ayaan Rahim ◽  
Lauryn Nakamitsu

<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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