Transductive polyhedral conic classifiers for machine learning applications

doi:10.1016/j.patrec.2022.07.001

Transductive polyhedral conic classifiers for machine learning applications

2022-09-01

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DOI：10.1016/j.patrec.2022.07.001

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In this paper, we introduce novel methods called Transductive Polyhedral Conic Classifiers that use both labeled and unlabeled data for classification. The proposed methodology utilizes the concave-convex procedure to solve the resulting optimization problems as in the Robust Transductive Support Vector Machines (RTSVMs). However, unlike RTSVM that uses SVM formulation, our proposed methods use the polyhedral conic classifier formulation that returns tight and closed decision boundaries compared to SVM. We tested the proposed methods on various datasets and experimental results show that our proposed methods yield better results than the existing transductive learning classifiers.(c) 2022 Elsevier B.V. All rights reserved.

Keywords:

Transductive learning

Polyhedral conic classifier

Large -margin classifier

Optimization

Transductive polyhedral conic classifiers for machine learning applications

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