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

Selective information enhancement learning for creating interpretable representations in competitive learning.

Ryotaro Kamimura1

  • 1IT Education Center, Tokai University, 1117 Kitakaname, Hiratsuka, Kanagawa 259-1292, Japan. ryo@keyaki.cc.u-tokai.ac.jp

Neural Networks : the Official Journal of the International Neural Network Society
|February 9, 2011
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...

You might also read

Related Articles

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

Sort by
Same author

Collective mutual information maximization to unify passive and positive approaches for improving interpretation and generalization.

Neural networks : the official journal of the International Neural Network Society·2017
Same author

Cooperation-controlled learning for explicit class structure in self-organizing maps.

TheScientificWorldJournal·2014
Same author

Self-enhancement learning: target-creating learning and its application to self-organizing maps.

Biological cybernetics·2011
Same author

Cooperative information maximization with Gaussian activation functions for self-organizing maps.

IEEE transactions on neural networks·2006
Same author

Unifying cost and information in information-theoretic competitive learning.

Neural networks : the official journal of the International Neural Network Society·2005
Same author

Multi-layered greedy network-growing algorithm: extension of greedy network-growing algorithm to multi-layered networks.

International journal of neural systems·2004

We developed selective information enhancement learning to interpret AI representations by identifying key variables. This method clarifies class boundaries using fewer, more informative inputs, improving AI model understanding.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Information Theory

Background:

  • Interpreting complex representations in machine learning models remains a challenge.
  • Identifying crucial variables for accurate classification is essential for model transparency.

Purpose of the Study:

  • To propose a novel information-theoretic method, selective information enhancement learning, for explicit interpretation of learned representations.
  • To enhance the clarity of class boundaries by focusing on a minimal set of important variables.

Main Methods:

  • Variable selection via information enhancement, measuring mutual information between input patterns and competitive units.
  • Retraining neural networks using free energy minimization with selected important variables.
  • Evaluating the method on artificial data, the Senate problem, and a voting attitude problem.

Related Experiment Videos

Main Results:

  • Achieved clear class boundaries using a reduced number of variables across all tested problems.
  • Demonstrated that a smaller subset of input variables often contains the majority of information.
  • Observed that this tendency is more pronounced in larger network architectures.

Conclusions:

  • Selective information enhancement learning effectively interprets AI representations by highlighting critical variables.
  • The method enhances model transparency and efficiency by reducing feature dimensionality.
  • This approach offers a pathway to more understandable and robust machine learning models.