Exemplar-based pattern recognition via semi-supervised learning

Anagnostopoulos, Georgios C.; Bharadwaj, M.; Georgiopoulos, Michael; Verzi, Stephen Joseph; Heileman, Gregory L.

doi:10.1109/ijcnn.2003.1224008

Cited by 20 publications

(25 citation statements)

References 10 publications

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“…Amongst them we refer to the work by Marriott and Harrisson (1995), where the authors eliminate the match tracking mechanism of Fuzzy ARTMAP when dealing with noisy data; the work by Charalampidis, et al (2001), where the Fuzzy ARTMAP equations are appropriately modified to compensate for noisy data; the work by Verzi, et al (2001), Anagnostopoulos, et al (2002bAnagnostopoulos, et al ( , 2003, and Gomez-Sanchez, et al (2002 & 2001), where different ways are introduced of allowing the Fuzzy ARTMAP categories to encode patterns that are not necessarily mapped to the same label; the work by Koufakou, et al (2001), where cross-validation is employed to avoid the overtraining/category proliferation problem in Fuzzy ARTMAP; and the work by Carpenter (1998), Williamson (1997), Parrado-Hernandez et al (2003), where the ART structure is changed from a winner-take-all to a distributed version and simultaneously slow learning is employed with the intent of creating fewer ART categories and reducing the effects of noisy patterns.…”

Section: Literature Reviewmentioning

confidence: 99%

GFAM: Evolving Fuzzy ARTMAP neural networks

et al. 2007

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This paper focuses on the evolution of Fuzzy ARTMAP neural network classifiers, using genetic algorithms, with the objective of improving generalization performance (classification accuracy of the ART network on unseen test data) and alleviating the ART category proliferation problem (the problem of creating more than necessary ART network categories to solve a classification problem). We refer to the resulting architecture as GFAM. We demonstrate through extensive experimentation that GFAM exhibits good generalization and is of small size (creates few ART categories), while consuming reasonable computational effort. In a number of classification problems, GFAM produces the optimal classifier. Furthermore, we compare the performance of GFAM with other competitive ARTMAP classifiers that have appeared in the literature and addressed the category proliferation problem in ART. We illustrate that GFAM produces improved results over these architectures, as well as other competitive classifiers.

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Section: Literature Reviewmentioning

confidence: 99%

GFAM: Evolving Fuzzy ARTMAP neural networks

et al. 2007

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“…Research on Adaptive Resonance Theory (ART) neural networks has been active over the last two decades and several architectures have been proposed over the years. For example, extensions to the classic Fuzzy ARTMAP classification model [9] include Hypersphere ARTMAP [10], Ellipsoid ARTMAP [11], BARTMAP-S [12] as well as semi-supervised Fuzzy ARTMAP (ssFAM) and Ellipsoid ARTMAP [13] to mention only a very few. MO-GART is capable of evolving populations of such types of ART-based classifiers to produce superior quality models.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective memetic evolution of ART-based classifiers

Mersch

Wen

et al. 2010

IEEE Congress on Evolutionary Computation

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Abstract-In this paper we present a novel framework for evolving ART-based classification models, which we refer to as MOME-ART. The new training framework aims to evolve populations of ART classifiers to optimize both their classification error and their structural complexity. Towards this end, it combines the use of interacting sub-populations, some traditional elements of genetic algorithms to evolve these populations and a simulated annealing process used for solution refinement to eventually give rise to a multi-objective, memetic evolutionary framework. In order to demonstrate its capabilities, we utilize the new framework to train populations of semi-supervised Fuzzy ARTMAP and compare them with similar networks trained via the recently published MO-GART framework, which has been shown as being very effective in yielding highquality ART-based classifiers. The experimental results show clear advantages of MOME-ART in terms of Pareto Front quality and density, as well as parsimony properties of the resulting classifiers.

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“…A number of authors have tried to address the category proliferation/over-training problem in Fuzzy ARTMAP. Amongst them we refer to the work by Mariott and Harrisson (Marriott & Harrison, 1995), where the authors eliminate the match tracking mechanism of Fuzzy ARTMAP when dealing with noisy data, the work by Charlampidis, et al, (Charalampidis, Kasparis, & Georgiopoulos, 2001), where the Fuzzy ARTMAP equations are appropriately modified to compensate for noisy data, the work by Verzi, et al, (Verzi, Heileman, Georgiopoulos, & Healy, 2001), Anagnostopoulos, et al, (Anagnostopoulos, Bharadwaj, Georgiopoulos, Verzi, & Heileman, 2003), and Gomez-Sanchez, et al, (Gomez-Sanchez, Dimitriadis, Cano-Izquierdo, & Lopez-Coronado, 2002), where different ways are introduced of allowing the Fuzzy ARTMAP categories to encode patterns that are not necessarily mapped to the same label, provided that the percentage of patterns corresponding to the majority label exceeds a certain threshold, the work by Koufakou, et al, (Koufakou, Georgiopoulos, Anagnostopoulos, & Kasparis, 2001), where cross-validation is employed to avoid the overtraining/category proliferation problem in Fuzzy ARTMAP, and the work by Carpenter (Carpenter & B. L. Milenova, 1998), Williamson (Williamson, 1997), Parrado-Hernandez, et al, (Parrado-Hernandez, Gomez-Sanchez, & Dimitriadis, 2003), where the ART structure is changed from a winner-take-all to a distributed version and simultaneously slow learning is employed with the intent of creating fewer ART categories and reducing the detrimental effects of noisy patterns.…”

Section: Introductionmentioning

confidence: 99%