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Practical Neural Network Recipies in C++
book

Practical Neural Network Recipies in C++

by Masters
June 2014
Intermediate to advanced content levelIntermediate to advanced
493 pages
20h 30m
English
Morgan Kaufmann
Content preview from Practical Neural Network Recipies in C++
Optimizing the
Decision Threshold
399
if (xnew > 1.0) // Bound to legal domain
xnew = 1.0 ;
if (xnew < 0.0)
xnew = 0.0 ;
ynew = parzen_crit ( nO , n1 , sO , s1 , wtO , wt1 , sigma , xnew ) ;
if (fabs(ynew) < bestcrit) { // Keep track of best
bestthresh = xnew ; // as secant method may
bestcrit = fabs(ynew) ; // not necessarily converge!
}
}
return bestthresh ;
}
Little has been said regarding the choice of σ, the window-
width parameter. Larger values result in loss of detail but more
immunity to sampling error. On page 379, where the subject was
confidences, it was suggested that σ be somewhere around 0.02 to 0.05.
This is good when importan ...
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Publisher Resources

ISBN: 9780080514338