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
Link To Document :
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