meljun cortes ibm spss neural networks
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Neural Networks
This contains my personal notes only
thus, this is not complete. Most of the
contents were taken from the training
manual of IBM SPSS Modeler. Please
refer to the training manual for acomplete discussion.
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Neural Networks
It attempts to solve problems using methods that is
similar on how the brains operate.
Think of how a parent teaches a child how to read.
Pattern of letterspresented to the
child
The child makes an attempt.
If the child is correct, then she is rewarded. Thenext time she sees the same combination of letters
she is likely to remember the correct response.
If the child is incorrect, then she is told the
correct response and tries to adjust her
response based on this feedback.
It starts with inputs. As the child receives the inputs, her
brains work and then produce outputs.
Neural networks work the same way!
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Neural Networks (NN)
Attempts to solve problems using
methods modeled on how the brain
operates.
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The input layer contains thefields used to predict theoutcome.
The output layer contains theoutput field (the target ofprediction)
The input and output fields canbe numeric or symbolic. Thesymbolic fields are transformedinto numeric (binary setcoding) before processing bythe network.
The hidden layer contains anumber of neurons at whichoutputs from previous layercombine. A network can haveany number of hidden layers
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Neural Networks look like below:
Neural network is learning the relationship between data and results
itis said to be training.
Once fully trained, the network can be given new unseen data and can
make a decision/prediction based upon its experience.
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Neural network consists of a number of
processing elements (called neurons) thatare arranged in layers. Each neuron is
linked to every neuron in the previous
layer by connections that have strengthsor weights attached to them. The learning
algorithm controls the adaptation of these
weights to the data; this gives the system
the capability to learn by example and
generalize for new situations.
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Main Consideration in building a network Locate
the Global Solution!
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Different types of Neural Networks
Multi-Layer Perceptron (MLP)
Radial Basis Function Network (RBFN)
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Multi-Layer Perceptron (MLP)
MLP networks consist of an input layer, an output layerand one or more hidden layers. The hidden layer isrequired to perform non-linear mappings.
Each hidden layer neuron receives an input based on a
weighted combination of the outputs of the neurons inthe previous layer.
The neurons within the final hidden layer are, in turn,combined to produce an output. This predicted value isthen compared to the correct output and the difference
between the two values (the error) is fed back into thenetwork, which in turn is updated. The feeding of theerror back through the network is referred to as back-propagation.
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Child learning the difference
between apple and orange: The child may decide in making a decision that the most
useful factors are the shape, color, and size of the fruit these are the inputs.
When shown the first example of a fruit she may look at
the fruit and decide that it is round, red in color and of aparticular size.
Not knowing of what an apple or an orange looks like,the child may decide to place equal importance on eachof such factors the importance is what a network refers
to as weights.
At this stage, the child is most likely to randomly chooseeither an apple or an orange for her prediction.
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On being told the correct response, the child will
increase or decrease the relative importance of
each of the factors to improve her decision
(reduce the error). In a similar fashion a MLP network begins with
random weights placed on each of the inputs.
On being told the correct response, the network
adjusts these internal weights. In time, the childand the network will hopefully make correct
predictions.
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MLP fits a non-linear curve
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The advantages of using a MLP are:
It is effective on a wide range of problems It is capable of generalizing well
If the data are not clustered in terms of their inputfields, it will classify examples in the extreme regions
It is currently the most commonly used type ofnetwork and there is much literature discussing itsapplications
The disadvantages of using a MLP are: It can take a great deal of time to train
It does not guarantee finding the best global solution
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MLP learning algorithms
Quick
Dynamic
Multiple Prune
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Radial Basis Function Network (RBFN)
It uses the k-means clusteringalgorithm to determine
the number and location of the centers in the input space
The RBF can be thought of
performing a type of clustering
within the input space, encircling
individual clusters of data by a
number of basis functions. If a data
point falls within the region of
activation of a particular basis
function, then the neuron
corresponding to that basis functionresponds most strongly.
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The advantages of using a RBF network are:
It is quicker to train than a MLP
It can model data that are clustered within the input
space.The disadvantages of using a RBF network are:
It is difficult to determine the optimal position of the
function centers
The resulting network often has a poor ability torepresent the global properties of the data.
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Which Method to Use?
When building neural networks it is
sensible to try both algorithms and
either choose the one with the bestoverall performance, or, use both
models to gain a majority prediction.
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Neural Network Hands-on
Data set: Churn.txt