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From: Apache Wiki <wikidiffs@apache.org>
To: Apache Wiki <wikidiffs@apache.org>
Date: Fri, 14 Jun 2013 03:22:01 -0000
Message-ID: <20130614032201.75240.3733@eos.apache.org>
Subject: 
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The "MultiLayerPerceptron" page has been changed by YexiJiang:
http://wiki.apache.org/hama/MultiLayerPerceptron?action=3Ddiff&rev1=3D15&re=
v2=3D16

  Node: This page is always under construction.
  =

  =3D=3D What is Multilayer Perceptron? =3D=3D
- A [[http://en.wikipedia.org/wiki/Multilayer_perceptron|multilayer percept=
ron]] is a kind of feed forward [[http://en.wikipedia.org/wiki/Artificial_n=
eural_network|artificial neural network]], which is a mathematic model insp=
ired by the biological neural network.
+ A [[http://en.wikipedia.org/wiki/Multilayer_perceptron|multilayer percept=
ron (MLP)]] is a kind of feed forward [[http://en.wikipedia.org/wiki/Artifi=
cial_neural_network|artificial neural network]], which is a mathematic mode=
l inspired by the biological neural network.
  The multilayer perceptron can be used for various machine learning tasks =
such as classification and regression.
  =

  The basic component of a multilayer perceptron is the neuron. =

  In a multilayer perceptron, the neurons are aligned in layers and in any =
two adjacent layers the neurons are connected in pairs with weighted edges.
- A practical multilayer perceptron consists of at least three layers of ne=
urons, including one input layer, one or more hidden layers, and one output=
 layer.
+ A practical multilayer perceptron consists of at least three layers of ne=
urons, including one input layer, one or more hidden layers, and one output=
 layer. =

+ =

+ The size of input layer and output layer determines what kind of data a M=
LP can accept.
+ Specifically, the number of neurons in the input layer determines the dim=
ensions of the input feature, the number of neurons in the output layer det=
ermines the dimension of the output labels. Typically, the two-class classi=
fication and regression problem requires the size of output layer to be one=
, while the multi-class problem requires the size of output layer equals to=
 the number of classes.
  =

  Here is an example multilayer perceptron with 1 input layer, 1 hidden lay=
er and 1 output layer:
  =

@@ -19, +22 @@

  =

  =3D=3D How Multilayer Perceptron works? =3D=3D
  =

- In general, people use the (already prepared) MLP by feed the input featu=
re to the input layer and get the result from the output layer.
+ In general, people use the (already prepared) MLP by feeding the input fe=
ature to the input layer and get the result from the output layer.
+ The results are calculated in a feed-forward approach, from the input lay=
er to the output layer.
+ =

  =

  To be added...
 =20