Add like
Add dislike
Add to saved papers

Dual vigilance fuzzy adaptive resonance theory.

Clusters retrieved by generic Adaptive Resonance Theory (ART) networks are limited to their internal categorical representation. This study extends the capabilities of ART by incorporating multiple vigilance thresholds in a single network: stricter (data compression) and looser (cluster similarity) vigilance values are used to obtain a many-to-one mapping of categories-to-clusters. It demonstrates this idea in the context of Fuzzy ART, presented as Dual Vigilance Fuzzy ART (DVFA), to improve the ability to capture clusters with arbitrary geometry. DVFA outperformed Fuzzy ART for the datasets in our experiments while yielding a statistically-comparable performance to another more complex, multi-prototype Fuzzy ART-based architecture.

Full text links

We have located links that may give you full text access.
Can't access the paper?
Try logging in through your university/institutional subscription. For a smoother one-click institutional access experience, please use our mobile app.

For the best experience, use the Read mobile app

Mobile app image

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app

All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.

By using this service, you agree to our terms of use and privacy policy.

Your Privacy Choices Toggle icon

You can now claim free CME credits for this literature searchClaim now

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app