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Support Vector Machines

2013
Fisher’s linear discriminant function (LDF) and related classifiers for binary and multiclass learning problems have performed well for many years and for many data sets. Recently, a brand-new learning methodology, support vector machines (SVMs), has emerged (Boser, Guyon, and Vapnik, 1992), which has matched the performance of the LDF and, in many ...
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Support Vector Machines

2001
Support vector machines are based on the theoretical learning theory developed by Vapnik [12], [17, pp. 92-129], [48], who defies the conventional belief that the optimal classification system can be developed using the optimally reduced features. In support vector machines, an n-class problem is converted into n two-class problems in which one class ...
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Support Vector Machines

2002
This chapter gives a short introduction to support vector machines, the basic learning method used, extended, and analyzed for text classification throughout this work. Support vector machines [Cortes and Vapnik, 1995][Vapnik, 1998] were developed by Vapnik et al.
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Viral vector platforms within the gene therapy landscape

Signal Transduction and Targeted Therapy, 2021
Phillip W L Tai, Guangping Gao
exaly  

Imaging the Néel vector switching in the monolayer antiferromagnet MnPSe3 with strain-controlled Ising order

Nature Nanotechnology, 2021
Zhuoliang Ni   +2 more
exaly  

Controlling vector-borne diseases by releasing modified mosquitoes

Nature Reviews Microbiology, 2018
Heather A Flores, Scott L O'neill
exaly  

Adeno-associated virus vector as a platform for gene therapy delivery

Nature Reviews Drug Discovery, 2019
Dan Wang, Phillip W L Tai, Guangping Gao
exaly  

Layer thickness dependence of the current-induced effective field vector in Ta|CoFeB|MgO

Nature Materials, 2012
Junyeon Kim   +2 more
exaly  

Hydrogen as an energy vector

Renewable and Sustainable Energy Reviews, 2020
Zainul Abdin   +2 more
exaly  

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