"KnowledgeMiner is the only product that I have found that
makes it easy to try non-standard equation formats on a
data set. Many standard regression tools are as easy, but
they limit you to a small set of potential relationships.
KnowledgeMiner combines spreadsheet-like set up with an
algorithm that doesn't "over fit" the model. Also, the output
is in a readily usable format (e.g. not C++ code)."
— Ware Adams, Dean & Company, a strategy consulting
firm in the U.S.
"The Alpine skiing and Athletic French Federation have
contacted my laboratory to build a profile of their elite
athletes. In this case, KnowledgeMiner helped me save a
lot of time and gave me models on the most important variables,
and pointed out the less relevant."
— Fabrice Viale, Doctoral thesis student
Laboratoire de Physiologie, Faculte de Medecine, France
"KnowledgeMiner is the most advanced implementation of
the GMDH approach now. It uses the inductive method, which
is different from deductive techniques used commonly for
modeling on principle. Many important successful results
were received using this tool recently. They show the advantage
of it over analogous well-known software."
— Prof. Alexey G. Ivakhnenko, author of the GMDH
approach
"It is really easy-to-use tool. It helps me to find laws
which acts in my object directly from the data sample only."
— Gregory Ivakhnenko, leading specialist at the
National Institute for Strategic Studies of Ukraina
"I like KnowledgeMiner because its algorithm does not make
any assumtions on the underlying data; well, at least not
during the initial model-building phase. I also like the
fact that it generates sets of equations that the user can
review with detailed understanding of the interactions and
dependencies of each variable. Also, the algorithm(s) behave
surprising well under extreme conditions for certain complex
dynamical systems. Congratulations for your excellent work."
— Alexis Pobedonostzeff, Pfizer Inc.
Director, Health Care Issues Analysis & Management
"I have purchased your program KnowledgeMiner and have
had some time to use it. My research is in artificial intelligence
applications in clinical medicine at the University of Western
Ontario in London, Canada. I have so far used backward error
propagation and probalistic ANNs for outcomes based research.
I also have some experience with fuzzy decision theory and
expert systems. Your program looks interesting and has some
advantages over my current modelling software (ie. NeuroSMARTs,
Brainmaker and Neuralyst). ... I wish to congratulate you
on your very promising software."
— Wayne, Associate Professor of Medicine, Division
of Cardiology, University of Western Ontario, London, Canada
"I'm a physicist by training, working as a radar engineer
on some cutting edge target recognition/classification technologies.
I am now using KM to circumvent all the past pattern recognition
algorithms which have been years (and millions of $) in
development by the armed forces. Although I am just now
starting to use KM in this application, my initial indications
are that KM is providing a more robust,complete and more
accurate classification capability than any of the previously
used algorithms, and with comparatively no effort on my
part"
— Herb, Vista Technologies, Inc.
"Lovely maths and algorithms. Nice and simple product.
Feel that it can significantly assist me. Looking forward
to understanding it better to put it to real use."
— Dr. Conrad Mackenzie, Australia
"I am Head of Computing and Information Technology at Katikati
College, a high school in New Zealand. A couple of weeks
ago I attended a big AppleFest at Rotorua with attendees
from about 120 different schools. One key speaker delivered
a workshop where he named the two big Mac products of 1998,
one was Myrmidon and the other one (of course) was KnowledgeMiner.
I have downloaded the demo and it appeals to me because
of my interest in AI in general and neural nets in particular."
— John, Katikati College, New Zealand
"I would just like to congratulate you on this program
on the behalf of Roger Bradbury who did some work on GMDH
back in 1988 (Green, D. G., Reichelt, R. E., and Bradbury,
R. H. (1988) Statistical Behaviour of the GMDH algorithm.
Biometrics 44: 49-69). He is happy that there is a modern
version of it - of which we will definitely be purchasing."
— Belinda, Bureau of Resource Sciences, Australia
"I believe that tools like this are definitely the start
of something very big in getting a handle on mountains of
information."
— Douglas, Dartmouth Medical School
