This paper provides a novel multi-class classification algorithm, which combines adaptive resonance theory with support vector machine principle.
This paper provides a novel multi-class classification algorithm, which combines adaptive resonance theory with support vector machine principle.
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Based on the principle of one-against-one support vector machines (SVMs) multi-class classification algorithm, this paper proposes an extended SVMs method ...
Aug 26, 2024 · Each classifier is trained to distinguish its respective class from all other classes combined. Prediction Phase: During prediction, each ...
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In this study, we develop an approach of combining machine learning techniques (Random Forest, Supported Vector Machine, and Naive Bayes) and land-as-an ...
Oct 22, 2024 · In this paper a novel architecture of Support Vector Machine classifiers utilizing binary decision tree (SVM-BDT) for solving multiclass ...
Currently there are two types of approaches for multi-class SVM. One is by constructing and combining several binary classifiers while the other is by directly ...
Support Vector Machines (SVMs) are state-of-the-art learning algorithms for classification problems due to their strong theoretical foundation and their good.
Dec 28, 2020 · This article comprises the application and comparison of supervised multi-class classification algorithms to a dataset, which involves the ...
This paper attempts to study and compare the classification performance if four supervised machine learning classification algorithms, viz., “Classification ...