Enhancing prototype reduction schemes with LVQ3-type algorithms

DOI10.1016/S0031-3203(02)00115-2zbMath1028.68148OpenAlexW2056609019WikidataQ60328066 ScholiaQ60328066MaRDI QIDQ1402668

Sang-Woon Kim, B. John Oommen

Publication date: 28 August 2003

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0031-3203(02)00115-2

zbMATH Keywords

CNN (condensed nearest neighbor)LVQ (learning vector quantization)PNN (prototypes for nearest neighbor classifier)Prototype reductionSVM (support vector machines)VQ (vector quantization)

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (7)

On using prototype reduction schemes to optimize dissimilarity-based classification ⋮ A class boundary preserving algorithm for data condensation ⋮ Simultaneous clustering and classification over cluster structure representation ⋮ On using prototype reduction schemes to optimize kernel-based nonlinear subspace methods. ⋮ On using prototype reduction schemes to enhance the computation of volume-based inter-class overlap measures ⋮ A simultaneous learning framework for clustering and classification ⋮ Prototype reduction schemes applicable for non-stationary data sets

Cites Work

• Unnamed Item
• Finding Prototypes For Nearest Neighbor Classifiers
• An algorithm for a selective nearest neighbor decision rule (Corresp.)
• Two Modifications of CNN
• Nearest prototype classifier designs: An experimental study

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