Academic Journal
The associative pattern classifier: Progress in theoretical understanding.
| Τίτλος: | The associative pattern classifier: Progress in theoretical understanding. |
|---|---|
| Συγγραφείς: | Valadez-Godínez, Sergio, Sossa, Humberto, Santiago-Montero, Raúl |
| Πηγή: | Journal of Intelligent & Fuzzy Systems; Feb2026, Vol. 50 Issue 2, p307-321, 15p |
| Θεματικοί όροι: | Pattern recognition systems, Static equilibrium (Physics), Fisher discriminant analysis, Vector algebra |
| Περίληψη: | The Associative Pattern Classifier (APC) was designed as an associative memory, focusing particularly on pattern classification. This implies that the training memory is constructed in a single operation and pattern classification also occurs in a single process. It is important to note that the APC translates the input patterns through a translation vector, which represents the average of all input patterns. Until now, there is no theoretical framework to explain the inner workings of the APC. Its relevance is inferred from the fact that several studies have been conducted using it as a foundation. This paper seeks to provide a theoretical comprehension of the APC's operation to facilitate future enhancements. We found the APC creates a system in static equilibrium through concurrent vectors at the origin (translation vector), resulting in a balanced separation of patterns. However, the APC cannot achieve complete pattern separation because of the presence of a neutral region. The neutral region is defined by all the points that define the separation hyperplanes. The points over the hyperplanes cannot be classified by the APC. Additionally, we discovered that the APC is unable to accurately classify the translation vector, which could be included as part of the input patterns. Our previous research showed that the APC is unsuccessful in achieving the linear separation of the AND function. In this research, we also broaden the examination of the AND function to illustrate that achieving linear separation is not feasible because the separation line represents a neutral region. The APC demonstrated exceptional performance when tested with artificial datasets where patterns were distributed over balanced regions, thus operating as an efficient multiclass and non-linear classifier. Nevertheless, the performance of the APC is lower when tested with real-world databases, making the APC inaccurate due to its restricted inner workings. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Intelligent & Fuzzy Systems is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 192308521 RelevancyScore: 1041 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1041.06896972656 |
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| Items | – Name: Title Label: Title Group: Ti Data: The associative pattern classifier: Progress in theoretical understanding. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Valadez-Godínez%2C+Sergio%22">Valadez-Godínez, Sergio</searchLink><br /><searchLink fieldCode="AR" term="%22Sossa%2C+Humberto%22">Sossa, Humberto</searchLink><br /><searchLink fieldCode="AR" term="%22Santiago-Montero%2C+Raúl%22">Santiago-Montero, Raúl</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Intelligent & Fuzzy Systems; Feb2026, Vol. 50 Issue 2, p307-321, 15p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Pattern+recognition+systems%22">Pattern recognition systems</searchLink><br /><searchLink fieldCode="DE" term="%22Static+equilibrium+%28Physics%29%22">Static equilibrium (Physics)</searchLink><br /><searchLink fieldCode="DE" term="%22Fisher+discriminant+analysis%22">Fisher discriminant analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Vector+algebra%22">Vector algebra</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The Associative Pattern Classifier (APC) was designed as an associative memory, focusing particularly on pattern classification. This implies that the training memory is constructed in a single operation and pattern classification also occurs in a single process. It is important to note that the APC translates the input patterns through a translation vector, which represents the average of all input patterns. Until now, there is no theoretical framework to explain the inner workings of the APC. Its relevance is inferred from the fact that several studies have been conducted using it as a foundation. This paper seeks to provide a theoretical comprehension of the APC's operation to facilitate future enhancements. We found the APC creates a system in static equilibrium through concurrent vectors at the origin (translation vector), resulting in a balanced separation of patterns. However, the APC cannot achieve complete pattern separation because of the presence of a neutral region. The neutral region is defined by all the points that define the separation hyperplanes. The points over the hyperplanes cannot be classified by the APC. Additionally, we discovered that the APC is unable to accurately classify the translation vector, which could be included as part of the input patterns. Our previous research showed that the APC is unsuccessful in achieving the linear separation of the AND function. In this research, we also broaden the examination of the AND function to illustrate that achieving linear separation is not feasible because the separation line represents a neutral region. The APC demonstrated exceptional performance when tested with artificial datasets where patterns were distributed over balanced regions, thus operating as an efficient multiclass and non-linear classifier. Nevertheless, the performance of the APC is lower when tested with real-world databases, making the APC inaccurate due to its restricted inner workings. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Intelligent & Fuzzy Systems is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3233/JIFS-219347 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 307 Subjects: – SubjectFull: Pattern recognition systems Type: general – SubjectFull: Static equilibrium (Physics) Type: general – SubjectFull: Fisher discriminant analysis Type: general – SubjectFull: Vector algebra Type: general Titles: – TitleFull: The associative pattern classifier: Progress in theoretical understanding. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Valadez-Godínez, Sergio – PersonEntity: Name: NameFull: Sossa, Humberto – PersonEntity: Name: NameFull: Santiago-Montero, Raúl IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10641246 Numbering: – Type: volume Value: 50 – Type: issue Value: 2 Titles: – TitleFull: Journal of Intelligent & Fuzzy Systems Type: main |
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