NNW
Applications
in
General
-
NNW's,
despite all appearances to the contrary,
are appearing in ever increasing numbers of real world applications and
are making real money:
-
OCR
(Optical Character Recognition)
-
Caere
Inc ($3M profit on $55M revenue in 1997) "OmniPage
Pro 6.0 significantly increases accuracy with its exclusive Quadratic Neural
Network(TM) (QNN) technology, an enhancement to its industry-leading OCR
engine..."
-
Data
Mining
- HNC
($23M profit on $110M revenue in 1997). Their flagship product is
Falcon.
"Falcon is a neural network-based system
that examines transaction, cardholder, and merchant data to detect
a wide range of credit card fraud...".
-
These
days a purchase of a new scanner typically includes a commercial OCR
program.
-
The algorithms
used are proprietary but most OCR programs are believed to use NNWs. (Calera,
started in 1986, did not admit to using NNW in its OCR programs until 1992
when Caere began advertising the use of them in its OCR products).
-
However,
the OCR example also illustrates why one cannot
claim NNWs are conquering the world.
-
One does
not feed the pixels of the picture file into a single
giant NNW and out pops the text.
-
To turn
a picture of text into a text file, a dozen
or more steps must be completed successfully
by the OCR program. For example, an OCR system might follow the step in
the diagram:
From Adaptive Solutions CNAPS user guide.
-
Designers
of OCR programs may choose NNWs to accomplish one
or more of these steps with NNWs while using
for other steps other techniques such as conventional AI (If-Then rules),
statistical models, hidden Markov models, etc.
-
The point
is that NNWs are becoming commonly used tools
but, just like other math techniques such as FFT and least squares fit,
they are still only tools, not the whole solution.
-
Few real
problems of interest can be
totally solved by a single
NNW.
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