Application of Support Vector Machines to Melissopalynological Data for Honey Classification

2010 ◽  
Vol 1 (2) ◽  
pp. 85-94 ◽  
Author(s):  
Giovanna Aronne ◽  
Veronica De Micco ◽  
Mario R. Guarracino
Keyword(s):  
Support Vector Machines ◽  
Statistical Learning ◽  
Geographical Origin ◽  
High Accuracy ◽  
Support Vector ◽  
Vector Machines ◽  
Innovative Solution ◽  
Z Scores ◽  
Sorrento Peninsula ◽  
Selection Of

In this paper, the authors address the problem of the discrimination of geographical origin and the selection of marker species of honeys using Support Vector Machines and z-scores. The methodology is based on the elaboration of palynological data with statistical learning methodologies. This innovative solution provides a simple yet powerful tool to detect the origin of honey samples. In case of honeys from Sorrento Peninsula, the discrimination from other Italian honeys is obtained with high accuracy.

Application of Support Vector Machines to Melissopalynological Data for Honey Classification

2012 ◽  
pp. 144-153
Author(s):  
Giovanna Aronne ◽  
Veronica De Micco ◽  
Mario R. Guarracino
Keyword(s):  
Support Vector Machines ◽  
Statistical Learning ◽  
Geographical Origin ◽  
High Accuracy ◽  
Support Vector ◽  
Vector Machines ◽  
Innovative Solution ◽  
Z Scores ◽  
Sorrento Peninsula ◽  
Selection Of

In this paper, the authors address the problem of the discrimination of geographical origin and the selection of marker species of honeys using Support Vector Machines and z-scores. The methodology is based on the elaboration of palynological data with statistical learning methodologies. This innovative solution provides a simple yet powerful tool to detect the origin of honey samples. In case of honeys from Sorrento Peninsula, the discrimination from other Italian honeys is obtained with high accuracy.

scholarly journals A Hybrid Barnacles Mating Optimizer Algorithm With Support Vector Machines for Gene Selection of Microarray Cancer Classification

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 64895-64905
Author(s):  
Essam H. Houssein ◽  
Diaa Salama Abdelminaam ◽  
Hager N. Hassan ◽  
Mustafa M. Al-Sayed ◽  
Emad Nabil
Keyword(s):  
Support Vector Machines ◽  
Gene Selection ◽  
Cancer Classification ◽  
Support Vector ◽  
Vector Machines ◽  
Selection Of

Study on Prediction of Mackey-Glass Chaotic Time Series Using Support Vector Machines

2014 ◽  
Vol 1051 ◽  
pp. 1009-1015 ◽  
Author(s):  
Ya Li Ning ◽  
Xin You Wang ◽  
Xi Ping He
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Support Vector Machines ◽  
Statistical Learning ◽  
Chaotic Time Series ◽  
Support Vector ◽  
Machine Type ◽  
Type Selection ◽  
Vector Machines ◽  
New Generation

Support Vector Machines (SVM), which is a new generation learning method based on advances in statistical learning theory, is characterized by the use of many standard technologies of machine learning such as maximal margin hyperplane, Mercel kernels and the quadratic programming. Because the best performance is obtained in many currently challenging applications, SVM has sustained wide attention, and has been become the standard tools of machine learning and data mining. But as a developing technology, SVM still have some problems and its applications are limited. In this paper, SVM and its applications in chaotic time series including predicting chaotic time series, focus on comparison in regression type selection, and kernel type selection in the same regression machine type.

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