T. Kasuba, "Simplified Fuzzy ARTMAP," AI Expert, pp. 18--25, November 1993. Home/Search Document Not in Database Summary Related Articles Check |
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Making Genome Expression Data Meaningful: Prediction.. - Francisco Azuaje.. (2000) (Correct)
....Objectives of this research This research aims to implement an automated approach to the prediction and discovery of classes of cancer based on the processing of gene expression data. The proposed technique consists of an artificial neural learning model known as Simplified Fuzzy ARTMAP (SFAM) [10], which addresses some of the weak aspects shown by traditional gene expression analysis methods. The objective of the prediction task is to distinguish normal subjects from those with DLBCL by using a number of genes with known or suspected roles in the development of the disease. The objective ....
....is applied to address the classification problems introduced in Section 1. Without going into details a SFAM is a version of the fuzzy ARTMAP neural network model [11] SFAM was designed to improve the computational efficiency of the fuzzy ARTMAP model with a minimal loss of learning effectiveness [10]. The fuzzy component in the name of this network refers to the fact that its learning process implements fuzzy logic operations in order to achieve a number of key pattern matching and adaptation functions. The essential two layer network architecture of a SFAM is depicted in Figures 1, 2 and ....
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T. Kasuba, "Simplified Fuzzy ARTMAP", AI Expert, November, 19-25, 1993.
A Computational Neural Approach to Support the.. - Francisco Azuaje.. (2000) (Correct)
....1.3 Aims of this research This research aims to implement an automated approach to the prediction and discovery of classes of cancer based on the processing of gene expression data. The proposed technique consists of an artificial neural learning model known as Simplified Fuzzy ARTMAP (SFAM) [14], which addresses some of the weak aspects shown by traditional gene expression analysis methods. This approach may provide an effective, efficient and inexpensive option to support diagnosis tasks and research. The objective of the prediction task is to distinguish normal subjects from those with ....
....a discussion of the results, and their implications to the process of molecular classification of cancer and gene expression analysis. This section also presents possible future work to be developed. 2. Introduction to the SFAM model A SFAM is a version of the fuzzy ARTMAP neural network model [14], 15] SFAM was designed to improve the computational efficiency of the fuzzy ARTMAP model with a minimal loss of learning effectiveness [14] The fuzzy component in the name of this network refers to the fact that its learning process implements fuzzy logic operations in order to achieve a ....
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T. Kasuba, Simplified Fuzzy ARTMAP, IEEE AI Expert, 19-25, November, 1993.
Pipelining of Fuzzy-ARTMAP (FAM) without Matchtracking.. - Castro, Secretan.. (2004) (Correct)
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T. Kasuba, "Simplified Fuzzy ARTMAP," AI Expert, pp. 18--25, November 1993.
Pipelining of Fuzzy ARTMAP without Matchtracking.. - Castro, Secretan, .. (Correct)
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Kasuba, T. (1993, November). Simplified Fuzzy ARTMAP. AI Expert, 18--25.
Data-Partitioning using the Hilbert Space Filling Curves.. - Castro, al. (Correct)
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Kasuba, T. (1993, November). Simplified Fuzzy ARTMAP. AI Expert, 18--25.
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T. Kasuba, "Simplified Fuzzy ARTMAP", AI Expert, Nov 1993.
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T. Kasuba. Simplified fuzzy ARTmap. AI Expert, 8(11):18-25, 1993.
Multisensor Image Fusion, Mining, and Reasoning.. - Chiarella, Fay.. (2004) (Correct)
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T. Kasuba, Simplified Fuzzy ARTMAP, AI Expert, pp. 18-25, Nov 1993.
Symbol grounding and a Neuro-Fuzzy architecture for multisensor.. - Bonarini (1 citation) (Correct)
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T. Kasuba, 1993. Simplified Fuzzy ARTMAP. AI EXPERT, November., pp. 18-25.
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