DocumentCode
1267404
Title
Comments on "parallel algorithms for finding a near-maximum independent set of a circle graph" [with reply]
Author
Steeg, E.W. ; Takefuji, Y. ; Lee, Kuan-Chou
Author_Institution
Dept. of Comput. Sci., Toronto Univ., Ont., Canada
Volume
2
Issue
2
fYear
1991
fDate
3/1/1991 12:00:00 AM
Firstpage
328
Lastpage
329
Abstract
The authors refers to the work of Y. Takefuji et al. (see ibid., vol.1, pp. 263-267, Sept. (1990)), which is concerned with the problem of RNA secondary structure prediction, and draws the reader´s attention to his own model and experiments in training the neural networks on small tRNA subsequences. The author admits that Takefuji et al. outline an elegant way to map the problem onto neural architectures, but suggests that such mappings can be augmented with empirical knowledge (e.g., free energy values of base pairs and substructures) and the ability to learn. In their reply, Y. Takefuji and K.-C. Lee hold that the necessity of the learning capability for the RNA secondary structure prediction is questionable. They believe that the task is to build a robust parallel algorithm considering more thermodynamic properties in the model.<>
Keywords
graph theory; learning systems; neural nets; parallel algorithms; RNA secondary structure prediction; circle graph; learning capability; mappings; near-maximum independent set; neural networks; parallel algorithm; Adaptive filters; Cognition; Distributed processing; Microstructure; Neural networks; Noise measurement; Noise reduction; Noise robustness; Parallel algorithms; RNA;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/72.80347
Filename
80347
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