MELJUN CORTES IBM SPSS Neural Networks

7/30/2019 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

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MELJUN CORTES IBM SPSS Neural Networks