Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Constrained learning vector quantization

H Yan1

  • 1Department of Electrical Engineering, University of Sydney, NSW, Australia.

International Journal of Neural Systems
|June 1, 1994
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

[Venetoclax combined with rituximab in the treatment of ibrutinib-resistant patient with chronic lymphocytic leukemia: a case report and literature reviews].

Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi·2019
Same author

[Problems and countermeasures of ocular trauma emergency management in China].

[Zhonghua yan ke za zhi] Chinese journal of ophthalmology·2019
Same author

Microscopy Instrumentation and Nanopositioning at NSLS-II: Current Status and Future Directions.

Synchrotron radiation news·2019
Same author

[Study on the association of dietary patterns of Shaanxi women of childbearing age during pregnancy with adverse pregnancy outcomes from 2010 to 2012].

Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine]·2019
Same author

[Interaction between pregnancy-induced hypertension and history of preterm birth on the risk of small for gestational age].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2019
Same author

Multimodal X-ray imaging of grain-level properties and performance in a polycrystalline solar cell.

Journal of synchrotron radiation·2019
Same journal

Latent Space Projections and Atlases, a Cautionary Tale in Deep Neuroimaging using Autoencoders.

International journal of neural systems·2026
Same journal

Transformer-Based Anomaly Detection for Neurodegenerative Screening in MRI Images.

International journal of neural systems·2026
Same journal

Discrete Wavelet Convolution for Learnable Time-Frequency Representation with Application to Seizure Prediction.

International journal of neural systems·2026
Same journal

Automatic Seizure Detection using Hierarchical Spectral-Temporal Feature Learning with an Imbalance-Aware Transformer.

International journal of neural systems·2026
Same journal

Pyramid Vision Transformer-Enhanced Conformer Network for Epileptic Seizure Recognition Using MultiChannel EEG Signals.

International journal of neural systems·2026
Same journal

A Time-Frequency Decoupled Contrastive Learning Framework for Electroencephalography-Based Parkinson's Disease Diagnosis.

International journal of neural systems·2026
See all related articles

Constrained Learning Vector Quantization (CLVQ) improves pattern recognition by preventing over-training. This novel method enhances classifier performance and reduces training time compared to standard Learning Vector Quantization (LVQ).

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Pattern Recognition

Background:

  • Learning Vector Quantization (LVQ) is an effective neural network technique for pattern recognition.
  • LVQ's performance is sensitive to learning parameter selection, and over-training can degrade classifier accuracy.
  • Existing methods often use prototype editing to optimize nearest neighbor classifiers (NNC).

Purpose of the Study:

  • Introduce Constrained Learning Vector Quantization (CLVQ) to improve LVQ performance.
  • Develop an efficient algorithm to implement the CLVQ constraint.
  • Evaluate CLVQ's effectiveness against standard LVQ through experimental results.

Main Methods:

  • Implemented a constraint in LVQ where coefficient updates are accepted only if recognition performance on training samples does not decrease.

Related Experiment Videos

  • Developed an efficient computer algorithm to manage this constraint during the iterative process.
  • Compared CLVQ with standard LVQ using experimental data.

    • Main Results:

    • CLVQ demonstrated superior performance compared to the standard LVQ method.
    • The CLVQ approach showed a significant reduction in required training time.
    • The constraint effectively prevented performance degradation due to over-training.

    Conclusions:

    • CLVQ offers an improved approach to pattern recognition using neural networks.
    • The proposed method enhances classifier accuracy and training efficiency over traditional LVQ.
    • CLVQ provides a robust alternative for pattern recognition tasks sensitive to parameter tuning.