Statistical analysis of the single-layer backpropagation algorithm with bias terms: mean weight behavior

Author(s):  
N.J. Bershad ◽  
J.J. Shynk
1994 ◽  
Vol 05 (03) ◽  
pp. 159-163
Author(s):  
R. LENDE ◽  
L.P. CSERNAI ◽  
D. KAMP

A backpropagation algorithm is used to train a neural net with the goal of distinguishing between two groups of biological species: prokaryotic and eukaryotic, based on frequencies of all 16 doublets in DNA sequences. An improvement of about 15% is obtained compared to statistical analysis based on one doublet only. This is done first by presenting sequences of species to the network with known classification (the training phase) and then showing species which the neural net has never seen before, and looking for the response. A brief discussion of the speed of training is given.


1966 ◽  
Vol 24 ◽  
pp. 188-189
Author(s):  
T. J. Deeming

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.


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