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feed forward neural network with backpropagation le
arning algorithm is considered as a black box
learning classifier since there is no certain inter
pretation or anticipation of the behavior of a neur
al
network weights. The weights of a neural network ar
e considered as the learning tool of the classifier
, and
the learning task is performed by the repetition mo
dification of those weights. This modification is
performed using the delta rule which is mainly used
in the gradient descent technique. In this article
a
proof is provided that helps to understand and expl
ain the behavior of the weights in a feed forward n
eural
network with backpropagation learning algorithm. Al
so, it illustrates why a feed forward neural networ
k is
not always guaranteed to converge in a global minim
um. Moreover, the proof shows that the weights in t
he
neural network are upper bounded (i.e. they do not
approach infinity). Data Mining, Delta
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