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One of the first impressive neural networks was NetTalk, which read in ASCII
text and correctly pronounced the words (producing phonemes which drove a
speech chip), even those it had never seen before.  Designed by John Hopkins
biophysicist Terry Sejnowski and Charles Rosenberg of Princeton in 1986,
this application made the Backprogagation training algorithm famous.  Using
the same paradigm, a neural network has been trained to classify sonar
returns from an undersea mine and rock.  This classifier, designed by
Sejnowski and R.  Paul Gorman, performed better than a nearest-neighbor
classifier.

The kinds of problems best solved by neural networks are those that people
are good at such as association, evaluation and pattern recognition.
Problems that are difficult to compute and do not require perfect answers,
just very good answers, are also best done with neural networks.  A quick,
very good response is often more desirable than a more accurate answer which
takes longer to compute.  This is especially true in robotics or industrial
controller applications.  Predictions of behavior and general analysis of
data are also affairs for neural networks.  In the financial arena, consumer
loan analysis and financial forecasting make good applications.  New network
designers are working on weather forecasts by neural networks (Myself
included).  Currently, doctors are developing medical neural networks as an
aid in diagnosis.  Attorneys and insurance companies are also working on
neural networks to help estimate the value of claims.

mesh.htm  view on Meta::CPAN

name a few.</P>
<P>One of the first impressive neural networks was NetTalk, which read in ASCII
text and correctly pronounced the words (producing phonemes which drove a
speech chip), even those it had never seen before.  Designed by John Hopkins
biophysicist Terry Sejnowski and Charles Rosenberg of Princeton in 1986,
this application made the Backprogagation training algorithm famous.  Using
the same paradigm, a neural network has been trained to classify sonar
returns from an undersea mine and rock.  This classifier, designed by
Sejnowski and R.  Paul Gorman, performed better than a nearest-neighbor
classifier.</P>
<P>The kinds of problems best solved by neural networks are those that people
are good at such as association, evaluation and pattern recognition.
Problems that are difficult to compute and do not require perfect answers,
just very good answers, are also best done with neural networks.  A quick,
very good response is often more desirable than a more accurate answer which
takes longer to compute.  This is especially true in robotics or industrial
controller applications.  Predictions of behavior and general analysis of
data are also affairs for neural networks.  In the financial arena, consumer
loan analysis and financial forecasting make good applications.  New network
designers are working on weather forecasts by neural networks (Myself
included).  Currently, doctors are developing medical neural networks as an
aid in diagnosis.  Attorneys and insurance companies are also working on
neural networks to help estimate the value of claims.</P>
<P>Neural networks are poor at precise calculations and serial processing. They