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

A self-organizing neural tree for large-set pattern classification.

H H Song1, S W Lee

  • 1Department of Computer Engineering Education, Andong National University, songchon-dong, Andong, Kyongbuk 760-749, Korea.

IEEE Transactions on Neural Networks
|February 7, 2008
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

[Effects of adipose stem cell-derived exosomes on rat tendon healing and its impact on the periphery neuropeptides expression].

Zhonghua yi xue za zhi·2025
Same author

[Value of contrast - enhanced ultrasonography and acoustic radiation force impulse elastography in identification of boundary range and viability of hepatic alveolar echinococcosis].

Zhongguo xue xi chong bing fang zhi za zhi = Chinese journal of schistosomiasis control·2020
Same author

[Factors associated with smoking cessation attempts in male current smokers in rural area of Shandong province].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2016
Same author

A unique surface-initiated property of nanoparticles and application for the synthesis of hybrid organic-inorganic nanoparticles.

Chemical communications (Cambridge, England)·2014
Same author

Comparison of RECIST 1.0 and RECIST 1.1 on computed tomography in patients with metastatic colorectal cancer.

Oncology·2014
Same author

Effects of in ovo injection of carbohydrate solution on small intestine development in domestic pigeons (Columba livia).

Journal of animal science·2013
Same journal

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

IEEE transactions on neural networks·2013
Same journal

Guest editorial: special section on white box nonlinear prediction models.

IEEE transactions on neural networks·2011
Same journal

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

IEEE transactions on neural networks·2011
Same journal

Guest editorial: special section on data-based control, modeling, and optimization.

IEEE transactions on neural networks·2011
Same journal

Neural network-based multiple robot simultaneous localization and mapping.

IEEE transactions on neural networks·2011
Same journal

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks·2011
See all related articles

This study introduces a Structurally Adaptive Intelligent Neural Tree (SAINT) to overcome limitations in classifying complex patterns. SAINT automatically determines optimal network structure and size for efficient, high-accuracy classification.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Pattern Recognition

Background:

  • Conventional neural networks face challenges with large-scale, complex pattern classification, including network design and computational load.
  • Determining optimal network structure and size is a significant hurdle for existing neural network models.

Purpose of the Study:

  • To propose a novel neural network architecture, the Structurally Adaptive Intelligent Neural Tree (SAINT), designed to address the limitations of conventional methods.
  • To develop a system capable of automatically adapting its structure for efficient classification of complex and large-scale pattern datasets.

Main Methods:

  • SAINT employs a hierarchical partitioning of input pattern space using a tree-structured network.
  • Subnetworks within the SAINT architecture possess topology-preserving mapping capabilities.

Related Experiment Videos

  • The core innovation lies in the automatic structure adaptation mechanism to determine suitable network size and topology.

    • Main Results:

    • SAINT demonstrated high effectiveness in classifying large sets of real-world handwritten characters exhibiting significant variations.
    • The model successfully handled multilingual, multifont, and multisize character datasets.
    • Experimental results confirm SAINT's ability to automatically find an appropriate network structure and size.

    Conclusions:

    • SAINT offers a robust solution for the classification of large-scale and complex patterns, overcoming the drawbacks of traditional neural networks.
    • The adaptive structure of SAINT allows for efficient and accurate classification, particularly for challenging datasets like diverse handwritten characters.
    • This approach advances the field of pattern recognition by providing an intelligent, self-optimizing neural network architecture.