A
neural network is a software that simulation of a biological brain. The purpose
of a neural network is to learn to recognize patterns in your
data. Once the neural network has been trained on samples of your data, it can
make predictions by detecting similar patterns in future data.
Neural
networks are a branch of the field known as "Artificial
Intelligence". Other branches include Case Based Reasoning, Expert
Systems, and Genetic Algorithms. Related fields include Classical Statistics,
Fuzzy Logic and Chaos Theory. A Neural network can be considered as a black box
that is able to predict an output pattern when it recognizes a given input
pattern. The neural network must first be "trained" by having it
process a large number of input patterns and showing it what output resulted
from each input pattern. Once trained, the neural network is able to recognize
similarities when presented with a new input pattern, resulting in a predicted
output pattern.
Neural
networks are able to detect similarities in inputs, even though a particular
input may never have been seen previously. This property allows for excellent
interpolation capabilities, especially when the input data is noisy (not
exact). Neural networks may be used as a direct substitute for autocorrelation,
multivariable regression, linear regression, trigonometric and other regression
techniques.
When
a data stream is analyzed using a neural network, it is possible to detect
important predictive patterns that were not previously apparent to a
non-expert. Thus the neural network can act as an expert.
An
Example of Neural Network: Bank Loans
Imagine
a highly experienced bank manager who must decide which customers will qualify
for a loan. His decision is based on a completed application form that contains
ten questions. Each question is answered by a number from 1 to 5.
Early
attempts at "Artificial Intelligence" took a simplistic view of this
problem. The Knowledge Engineer would interview the bank manager(s) and decide
that question one is worth 30 points, question two is worth 10 points, question
three is worth 15 points,...etc. Simple arithmetic was used to determine the
applicant's total rating. A hurdle value was set for successful applicants.
This approach helped to give artificial intelligence a bad name.
The
problem is that most real-life problems are non-linear in nature. Response #2
may be meaningless if both response #8 and #9 are high. Response #5 should be
the sole criterion if both #7 and #8 are low.
Our
ten question application has almost 10 million possible responses. The bank
manager's brain contains a Neural Network that allows him to use "Intuition".
Intuition will allow the bank manager to recognize certain similarities and
patterns that his brain has become attuned to. He may never have seen this
exact pattern before, but his intuition can detect similarities, as well as
dealing with the non-linearities. He is probably unable to explain the very
complex process of how his intuition works. A complicated list of rules
(called "Expert System") could be drawn up but these
rules may give only a rough approximation of his intuition.
If
we had a large number of loan applications as input, along with the manager's
decision as output, a neural network could be "trained" on these
patterns. The inner workings of the neural network have enough mathematical
sophistication to reasonably simulate the expert's intuition.
References
An Introduction
to Neural Networks. (n.d.). An Introduction to Neural Networks.
Retrieved February 14, 2014, from
http://www.cs.stir.ac.uk/~lss/NNIntro/InvSlides.html
What is a Neural
Network. (n.d.). What is a Neural Network. Retrieved February 14, 2014,
from http://www.cormactech.com/neunet/whatis.html
Neural Networks.
(n.d.). Neural Networks. Retrieved February 14, 2014, from
http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html
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