Java Data Mining Concepts - Java Data Mining: Strategy, Standard, and Practice - page 144

produce an ouput, which may be subject to an activation function . An
activation function effectively is a transformation on the output,
which includes the specification of a threshold above which the out-
put is 1, otherwise zero. Figure 7-5(a) illustrates a neuron that takes
x 1, x 2, and x 3 input value and w 1, w 2, and w 3 as input weights to
produce output value y .
Back propagation is the most common neural network learning
algorithm. It learns by iteratively processing the build data, comparing
the network's prediction for each case with the actual known target
value from the validation data . 3 For each case, the weights are updated
in the opposite direction, so as to minimize the error between the
network's prediction and actual target value.
Figure 7-5(b) illustrates a back propagation neural network that con-
sists of three types of layers: input, hidden , and output . The input
layer will have a number of neurons equal to the number of input
attributes, the output layer will have a number of neurons equal to
number of target values. The number of hidden layers and number
of neurons in each hidden layer can be determined by the algorithm
or specified by the data miner explicitly. In general, the addition of
a hidden layer can allow the network to learn more complex
patterns, but it can also adversely affect model build and apply
performance. For each neural layer, JDM allows specifying an
activation function that computes the activation state of each neuron

N (0,1) W (1,1,1)

W (1,1,2)

x 1

N (m-1,1)

N (1,1) N (m,1) y 1

•••

N (0,2)

x 2

N (m-1,2)

x 1
x 2
x 3

w 1 N (1,2) N (m,2) y 2

W (1,3,2)

w 2

Neuron

y

N (0,3)

x 3

w 3

Input Layer Hidden Layer(s) Output Layer

(a) (b)

Figure 7-5 Neural networks: (a) Neuron representation, (b) back propagation neural

networks.

3 Validation data is a kind of test data used during model building, which the

algorithm may automatically create by partitioning the build data. Validation
data allows the algorithm to determine how well the model is learning the pat-
terns in the data. JDM allows users to provide an evaluation dataset explicitly in
a build task, if desired.