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Abstract: Several state-of-the-art techniques: a neural network, Bayesian neural network, support vector machine and naive Bayesian classifier are experimentally evaluated in discriminating fluorescence in-situ hybridization (FISH) signals. Highly-accurate classification of signals from real data and artifacts of two cytogenetic probes (colours) is required for detecting abnormalities in the data. More than 3,100 FISH signals are classified by the techniques into colour and as real or artifact... (Update)
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BibTeX entry: (Update)
B. Lerner and N. D. Lawrence, "A comparison of state-of-the-art classification techniques with application to cytogenetics," Neural Comput., vol. 10, pp. 39--47, 2001. http://citeseer.ist.psu.edu/article/lerner01comparison.html More
@misc{ lerner01comparison,
author = "B. Lerner and N. Lawrence",
title = "A comparison of state-of-the-art classification techniques with application
to cytogenetics",
text = "B. Lerner and N. D. Lawrence, A comparison of state-of-the-art classification
techniques with application to cytogenetics, Neural Comput., vol. 10, pp.
39--47, 2001.",
year = "2001",
url = "citeseer.ist.psu.edu/article/lerner01comparison.html" }
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