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*Position_1(for weight initialization)* for i=1:num_of_loops *Position_2(for weight initialization)* - repeating cross validation for i=1:num_of_kfolds *Position_3(for weight initialization)* - Cross validation loop end end
ANSWER
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To help understanding, I will assume Nval = Ntst = 0. Search for the nonzero examples in the NEWSGROUP and ANSWERS. To design a typical I-H-O net with Ntrn training examples, try to not let the number of unknown weights Nw = (I+1)*H+(H+1)*O
exceed the number of training equations
Ntrneq = Ntrn*O
This will occur as long as H <= Hub where Hub is the upperbound
Hub = -1+ceil( (Ntrneq-O) / (I+O+1) )
Based on Ntrneq and Hub I decide on a set of numH candidate values for H
0 <= Hmin:dH:Hmax <= Hmax
numH = numel(Hmin:dH:Hmax)
and the number of weight initializations for each value of H, e.g.,
Matlabsolutions.com provide latest MatLab Homework Help,MatLab Assignment Help for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research.
Nw = (I+1)*H+(H+1)*O
exceed the number of training equations
Ntrneq = Ntrn*O
This will occur as long as H <= Hub where Hub is the upperbound
Hub = -1+ceil( (Ntrneq-O) / (I+O+1) )
Based on Ntrneq and Hub I decide on a set of numH candidate values for H
0 <= Hmin:dH:Hmax <= Hmax numH = numel(Hmin:dH:Hmax)
and the number of weight initializations for each value of H, e.g.,
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