On the minimal redundancy of binary error-correcting codes, Infor- mation and Control 28 (1975), no. 4, 268-291, Translated from the Russian (Problemy Peredachi Informatsii 10 (1974), no. 2, 26-42).V. I. Levenshtein, “On minimal redundancy of binary error-correcting codes,” Probl....
Coding design name, specified as a value in the following table. The table summarizes the coding schemes. ValueNumber of Binary LearnersDescription 'allpairs'and'onevsone'K(K– 1)/2For each binary learner, one class is positive, another is negative, and the software ignores the rest. This ...
I. Solov’eva, “Reconstructing Extended Perfect Binary One-Error-Correcting Codes from Their Minimum Distance Graphs,” IEEE Trans. Inform. Theory 55 , 2622–2625 (2009). MathSciNetI. Yu. Mogilnykh, P. R. J. O¨ sterg˚ard, O. Pottonen, and F. I. Solov'eva, Reconstruct- ing...
A general method of constructing error correcting binary group codes is obtained. A binary group code with n places, k of which are information places is called an (n,k) code. An explicit method of constructing t-error correcting (n,k) codes is given for n = 2m−1 and k = 2m−...
According to (25), the set J is shown in Table 1. Henceforth, using (26), the identified length of the code words is n˜ = 15. Analysis and performances The aim of our proposed algorithm is to blindly iden- tify the length of non-binary code words in noisy envi- ronment. This ...
Mdl = fitclinear(Tbl,Y) returns a linear classification model using the predictor variables in the table Tbl and the class labels in vector Y. Mdl = fitclinear(X,Y,Name,Value) specifies options using one or more name-value pair arguments in addition to any of the input argument combinations...
Full size table A compilation of parameter sets of all the three derived codes for small values of \ell , t is provided in Table 2. The table also provides a comparison of k with respective information-theoretic bound. 6 Conclusion and future work Binary constant weight codes find extensive ...
Mdl = fitclinear(Tbl,Y) returns a linear classification model using the predictor variables in the table Tbl and the class labels in vector Y. Mdl = fitclinear(X,Y,Name,Value) specifies options using one or more name-value pair arguments in addition to any of the input argument combinations...
Mdl = fitclinear(Tbl,Y) returns a linear classification model using the predictor variables in the table Tbl and the class labels in vector Y. Mdl = fitclinear(X,Y,Name,Value) specifies options using one or more name-value pair arguments in addition to any of the input argument combinations...
Mdl = fitclinear(Tbl,Y) returns a linear classification model using the predictor variables in the table Tbl and the class labels in vector Y. Mdl = fitclinear(X,Y,Name,Value) specifies options using one or more name-value pair arguments in addition to any of the input argument combinations...