Neural networks are a newly proliferating technique in desktop quantitative analysis. Neural network software adds artificial intelligence to data analysis by using algorithms that imitate elementary brain cell functions to “learn” the structure of data; that is, to discern patterns in the data. The software can then use its new “knowledge” to predict the outcome of problems involving similar data. The computational process of a neural network is fully automated.
Qualitative and Quantitative Analysis
Neural network software can be used to solve two basic kinds of
problems: (1) classification problems in which the investigator is
trying to determine in what category an unknown entity belongs,
for instance, a certain medical condition or whether a borrower
is likely to repay a loan; and (2) numeric problems in which the
investigator needs to predict a specific numeric value, such as
the age of a living organism or the level of sales during some
future period.
The Neural Networks Advantage
The difference between conventional problem solving
by computer and neural network computing is that with
conventional problem solving we make certain assumptions
about the structure of our data, allowing us to use a relatively
simple model of dependencies. However, our data often do
not match the assumptions, resulting in inaccurate solutions.
Neural networks are sophisticated systems with highly
interconnected elements, capable of modeling complex
relationships within data where conventional methods fail.
The two approaches are not competing, but complement
each other.
More Than 60 Years of History
The term “neural network” refers to the brain cell called a
neuron, because like its human counterpart, a computational
neuron receives input from a number of sources and responds
with a single output. Somewhat surprisingly, neural networks
are not new, and not even recent. The first artificial neuron was
proposed in 1943 by the neurophysiologist Warren McCulloch
(University of Illinois, Chicago and MIT) and the logician Walter
Pitts (MIT). But their invention did not find its purpose until the
advent of high-speed computing.
Real-World Applications
The current commercial and scientific environments in which
computer systems harvest enormous bodies of data are
ideal for the deployment of neural networks. The technique is
accessible to many professionals who do not have high-level
computing expertise. Neural network software is currently
used in such applications as: the detection of fraud in credit
card transactions, prediction of loan repayment, investment
performance prediction, diagnosis of tumors, identification
of unknown biological specimens, and prediction of products
failing safety standards.
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