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Sample reduction using farthest boundary point estimation (FBPE) for support vector data description (SVDD)
- Source :
- Pattern Recognition Letters. 131:268-276
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- The objective of this paper is to design an algorithm to maximize the learning ability and knowledge about the target class while minimizing the number of training samples for support vector data description (SVDD). With this motivation, a novel training sample reduction algorithm is proposed in this paper that selects the most promising boundary data points as training set. The proposed approach uses the local geometry of the distribution to estimate the farthest boundary points (also known as extreme points). The legitimacy of the proposed algorithm is verified via experiments performed on MNIST, Iris, UCI default credit card, svmguide and Indian Pines datasets.
- Subjects :
- Training set
Computer science
business.industry
Boundary (topology)
Sample (statistics)
Pattern recognition
02 engineering and technology
01 natural sciences
Class (biology)
Support vector machine
Reduction (complexity)
Credit card
Artificial Intelligence
0103 physical sciences
Signal Processing
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Artificial intelligence
010306 general physics
business
Software
MNIST database
Subjects
Details
- ISSN :
- 01678655
- Volume :
- 131
- Database :
- OpenAIRE
- Journal :
- Pattern Recognition Letters
- Accession number :
- edsair.doi...........fd056d79befd4a55b699c174534d4eb7