Privacy preserving and fast decision for novelty detection using support vector data description

DOI10.1007/S00500-014-1331-8zbMath1382.68194OpenAlexW2048629639MaRDI QIDQ1708732

Yong Liu, Wenhao Ying, Wenjun Hu, Fu-lai Chung, Shi-Tong Wang

Publication date: 26 March 2018

Published in: Soft Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00500-014-1331-8

zbMATH Keywords

novelty detectiondecision complexityintegrated squared error (ISE)kernelized spherepre-image of sphere centersupport vector data description (SVDD)

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

Belief-based chaotic algorithm for support vector data description

Uses Software

• UCI-ml
• FRSDE

Cites Work

• Unnamed Item
• Support vector data description
• Support-vector networks
• The support vector machine based on intuitionistic fuzzy number and kernel function
• Reidentification and \(k\)-anonymity: a model for disclosure risk in graphs
• FRSDE: Fast reduced set density estimator using minimal enclosing ball approximation
• A Parallel Mixture of SVMs for Very Large Scale Problems
• k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
• (t,k)-Diagnosis for Component-Composition Graphs under the MM* Model

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