We proposed the ensemble method based on different implementations of SVM, because they have large diversities by their different implementing methods. The ...
We proposed the ensemble method based on different implementations of SVM, because they have large diversities by their different implementing methods. The.
We proposed the ensemble method based on different implementations of SVM, because they have large diversities by their different implementing methods. The ...
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Sep 30, 2024 · This study aimed to classifying wheat genotypes using support vector machines (SVMs) improved with ensemble algorithms and optimization techniques.
Missing: Implementations Diversified
By incorporating the orthogonality constraints to diversify the CVM ensembles, this turns out to speed up the maximum margin discriminant analysis (MMDA) ...
This paper proposes a new method how to measure diversity and ensemble for linear kernel Support Vector Machine, which is based on the characteristic parameters ...
This work proposed the ensemble method based on different implementations of SVM, because they have large diversities by their different implementing ...
Ensemble learning is another example of how we design better learning algorithms. The random forest model is a particular case of ensemble models. · AdaBoost ...
Missing: Implementations Diversified
Oct 22, 2024 · To fill this void, this paper analyses and compares SVM ensembles with four different ensemble constructing techniques, namely bagging, AdaBoost ...
Mar 15, 2023 · This paper presents two novel ensemble approaches: probabilistic ordering of one-vs-rest (OVR) SVMs with naïve Bayes classifier and multiple decision templates ...