Jove
Visualize
Contact Us

Related Experiment Videos

A fast nearest neighbor classifier based on self-organizing incremental neural network.

Furao Shen1, Osamu Hasegawa

  • 1The State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210093, PR China. frshen@nju.edu.cn

Neural Networks : the Official Journal of the International Neural Network Society
|August 6, 2008
PubMed
Summary

The Adjusted SOINN Classifier (ASC) is a novel, fast nearest neighbor algorithm. It efficiently classifies data using adaptive prototypes and shows superior performance over other methods.

Related Concept Videos

Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

You might also read

Related Articles

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

Sort by
Same author

Differential diagnosis of retroperitoneal lymphangioma mimicking an ovarian tumor in the right adnexal region in a woman with a giant fibroid: a case report and literature review.

Fukushima journal of medical science·2026
Same author

Hemiparkinsonism Associated with Venous Anomaly-related Basal Ganglia Calcification.

Internal medicine (Tokyo, Japan)·2026
Same author

Dual prototypes for adaptive pre-trained model in class-incremental learning.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

IPF-RDA: An Information-Preserving Framework for Robust Data Augmentation.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Region-guided attack on the segment anything model.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

AdaAugment: A Tuning-Free and Adaptive Approach to Enhance Data Augmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025
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

Area of Science:

  • Computer Science
  • Machine Learning
  • Artificial Intelligence

Background:

  • Nearest neighbor classifiers are fundamental in machine learning.
  • Prototype-based classifiers offer advantages in interpretability and efficiency.
  • Existing methods may struggle with automatic prototype selection and incremental learning.

Purpose of the Study:

  • To introduce the Adjusted SOINN Classifier (ASC), a novel fast prototype-based nearest neighbor classifier.
  • To demonstrate ASC's ability to automatically determine the optimal number of prototypes.
  • To evaluate ASC's robustness to noisy data and its efficiency in classification.

Main Methods:

  • The Adjusted SOINN Classifier (ASC) is based on the self-organizing incremental neural network (SOINN) architecture.

Related Experiment Videos

  • ASC automatically learns the necessary number of prototypes for decision boundary determination.
  • The classifier is designed for incremental learning, preserving previously acquired knowledge.

    • Main Results:

    • ASC demonstrated robust performance on both artificial and real-world datasets.
    • Comparative analysis showed ASC achieved superior classification error rates.
    • ASC exhibited a high speed-up ratio, indicating very fast classification capabilities.

    Conclusions:

    • The Adjusted SOINN Classifier (ASC) is a highly efficient and effective prototype-based classifier.
    • ASC offers significant advantages in terms of speed, accuracy, and data compression.
    • ASC represents a promising advancement for nearest neighbor classification tasks.