Abstract

A neural network model replaces the supervised labeling component of a supervised learning system with an incremental cluster validity index-based unsupervised labeling component. An implementation is presented combining fuzzy adaptive resonance theory predictive mapping (ARTMAP) and incremental cluster validity indices (iCVI) for unsupervised machine learning purposes, namely the iCVIARTMAP. An iCVI module replaces the adaptive resonance theory (ART) module B of a fuzzy ARTMAP neural network model and provides assigmnents of input samples to clusters (i.e., labels) at each learning iteration in accordance to any of several possible iCVI methods described. A map field incrementally builds a many-to-one mapping of the categories of ART module A to the cluster labels. At the end of each learning epoch, clusters may be merged and/or split using the iCVI, which is recomputed incrementally except for the newly cluster during a split. The iCVI-ARTMAP performs ofl.line incremental multi-prototype-based clustering driven by the iCVI.

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Comments

Docket Number: 21MST-018

Filing Date: 08/16/2021

Issue Date: 03/04/2025, FY25

References Cited

U.S> Patent Documents:

9,043,326 B2 5/2015 Wunsch, II et al.

Prior Publication Data:

US 2022/0318632 A1 October 6, 2022

Patent Application Number

17/402,938

Patent Number

US 12,242,970 B2

Document Type

Patent

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2026 Guise AI, Inc., All rights reserved

Additional Rights Information

Assignee: Guise AI, Inc., Rolla, MO (US)

Publication Date

4 March, 2025

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