Properties of learning in ARTMAP

Georgiopoulos, Michael; Huang, Juxin; Heileman, Gregory L.

doi:10.1016/0893-6080(94)90083-3

Cited by 24 publications

(7 citation statements)

References 10 publications

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“…In particular, an upper bound is derived on the number of epochs required by a P -valued SAHN to learn a list of input-output pairs that is repeatedly presented to the architecture. Note that the properties of learning discovered in (Georgiopoulos et al, 1994) for ARTMAP are special cases of our results.…”

Section: Introductionsupporting

confidence: 50%

Supervised Adaptive Hamming Net for Classification of Multiple-Valued Patterns

Hung

Lin

1997

Int. J. Neur. Syst.

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A Supervised A daptive Hamming Net (SAHN) is introduced for incremental learning of recognition categories in response to arbitrary sequences of multiple-valued or binary-valued input patterns. The binary-valued SAHN derived from the Adaptive Hamming Net (AHN) is functionally equivalent t o a simplied ARTMAP, which is specically designed to establish many-to-one mappings. The generalization to learning multiple-valued input patterns is achieved by incorporating multiple-valued logic into the AHN. In this paper, we examine some useful properties of learning in a P-valued SAHN. In particular, an upper bound is derived on the number of epochs required by the P-valued SAHN to learn a list of input-output pairs that is repeatedly presented to the architecture. Furthermore, we connect the P-valued SAHN with the binary-valued SAHN via the thermometer code. Fast learning: When the Jth node in MAXNET is chosen, the template W J is updated according to the equation W new J = W old J u X :The adaptive w eight w J is updated according to the equationŵ new J

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Section: Introductionsupporting

confidence: 50%

Supervised Adaptive Hamming Net for Classification of Multiple-Valued Patterns

Hung

Lin

1997

Int. J. Neur. Syst.

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“…When Fuzzy ARTMAP is allowed to fully stabilize, it will create enough left-side rectangles so that each training sample is correctly classified (assuming that the training data is self-consistent). Binary-valued Fuzzy ARTMAP is guaranteed to stabilize in n epochs for n-dimensional feature space data (Georgiopoulos, et al, 1994).…”

Section: Fuzzy Artmapmentioning

confidence: 99%

Yucca Mountain licensing support network archive assistant.

Dunlavy¹,

Bauer²,

Verzi³

et al. 2008

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This report describes the Licensing Support Network (LSN) Assistant-a set of tools for categorizing e-mail messages and documents, and investigating and correcting existing archives of categorized e-mail messages and documents. The two main tools in the LSN Assistant are the LSN Archive Assistant (LSNAA) tool for recategorizing manually labeled e-mail messages and documents and the LSN Realtime Assistant (LSNRA) tool for categorizing new e-mail messages and documents. This report focuses on the LSNAA tool.There are two main components of the LSNAA tool. The first is the Sandia Categorization Framework, which is responsible for providing categorizations for documents in an archive and storing them in an appropriate Categorization Database. The second is the actual user interface, which primarily interacts with the 4 Categorization Database, providing a way for finding and correcting categorizations errors in the database.A procedure for applying the LSNAA tool and an example use case of the LSNAA tool applied to a set of e-mail messages are provided. Performance results of the categorization model designed for this example use case are presented.5

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“…Normalization of the components of the input patterns has been a common practice in the neural network literature. Complementary encoding of the input vector is necessary for the successful training of FAM (see Georgiopoulos, et al, 1994 …”

Section: The Fuzzy Artmap Architecturementioning

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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Properties of learning in ARTMAP

Cited by 24 publications

References 10 publications

Supervised Adaptive Hamming Net for Classification of Multiple-Valued Patterns

Supervised Adaptive Hamming Net for Classification of Multiple-Valued Patterns

Yucca Mountain licensing support network archive assistant.

GFAM: Evolving Fuzzy ARTMAP neural networks

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