One novel class of Bézier smooth semi-supervised support vector machines for classification

The semi-supervised support vector machine (S 3 VM) for classification is introduced for dealing with quantities of unlabeled data in the real world. Labeled data are utilized to train the algorithm and then were adapted to classify the unlabeled data. However, this algorithm has several drawbacks,...

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Bibliographic Details
Published in:Neural computing & applications 2021-08, Vol.33 (16), p.9975-9991
Main Authors: Wang, En, Wang, Zi-Yang, Wu, Qing
Format: Article
Language:English
Subjects:
Algorithms
Approximation
Artificial Intelligence
Classification
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Datasets
Image Processing and Computer Vision
Mathematical analysis
Original Article
Probability and Statistics in Computer Science
Quadratic programming
Quasi Newton methods
Support vector machines
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Summary:The semi-supervised support vector machine (S 3 VM) for classification is introduced for dealing with quantities of unlabeled data in the real world. Labeled data are utilized to train the algorithm and then were adapted to classify the unlabeled data. However, this algorithm has several drawbacks, such as the non-smooth term of semi-supervised objective function negatively affects the classification precision. Moreover, it is required to endure heavy burden in solving two quadratic programming problems with inversion matrix operation. To cope with this problem, this article puts forward a novel class of Bézier smooth semi-supervised support vector machines (BS 4 VMs), based on the approximation property of Bézier function to the non-smooth term. Because of this approximation, a fast quasi-Newton method for solving BS 4 VMs can be used to decrease the calculating time scale. This new kind of algorithm enhances the generalization and robustness of S 3 VM for nonlinear case as well. Further, to show how the BS 4 VMs can be practically implemented, experiments on synthetic, UCI dataset, USPS dataset, and large-scale NDC database are offered. The theoretical analysis and experiments comparisons clearly confirm the superiority of BS 4 VMs in both classification accuracy and calculating time.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-021-05765-6