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Ben Peter Yuhas

from Marietta, NY
Age ~65

Ben Yuhas Phones & Addresses

  • 2336 Olanco Rd, Marietta, NY 13110 (315) 636-7293
  • Atlantic Beach, FL
  • 121 Hawthorne Ave, Baltimore, MD 21210 (410) 467-9381 (410) 467-9387
  • Monroe, NJ
  • Pompey, NY
  • Long Beach, CA
  • Sparta, NJ
  • Marcellus, NY
  • Peapack, NJ

Work

Company: Yuhas consulting group llc Address: 121 Hawthorne Rd, Baltimore, MD 21210 Phones: (410) 467-9387 Position: President Industries: Business Consulting Services

Business Records

Name / Title
Company / Classification
Phones & Addresses
Ben Yuhas
President
Yuhas Consulting Group LLC
Business Consulting Services
121 Hawthorne Rd, Baltimore, MD 21210
Ben Yuhas
President
Yuhas Consulting Group LLC
Business Consulting Services
121 Hawthorne Rd, Baltimore, MD 21210
Ben Yuhas
President
Yuhas Consulting Group LLC
Business Consulting Services
121 Hawthorne Rd, Baltimore, MD 21210
(410) 467-9387
Ben Yuhas
Director of Data Processing
FIRST ANNAPOLIS CONSULTING INC
Management Consulting Services · Accountant · Process & Logistics Consulting Svcs · Business Management Consultant
3 Park Pl STE 200, Annapolis, MD 21401
900 Elkridge Lndg Rd, Linthicum, MD 21090
(410) 855-8500, (605) 347-8689, (410) 855-8599

Publications

Isbn (Books And Publications)

Neural Networks in Telecommunications

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Author

Ben Yuhas

ISBN #

0792394178

Us Patents

Method For Computing Models Based On Attributes Selected By Entropy

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US Patent:
7107192, Sep 12, 2006
Filed:
Mar 31, 1999
Appl. No.:
09/282619
Inventors:
Quan G. Cung - Austin TX, US
Harry Roger Kolar - Scottsdale AZ, US
Kevin Eric Norsworthy - Austin TX, US
Julio Ortega - The Colony TX, US
Frederick J. Scheibl - Austin TX, US
Vasken Torossian - Round Rock TX, US
Ben Peter Yuhas - Baltimore MD, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G06F 17/10
US Classification:
703 2, 382224, 703 1, 703 6, 703 22
Abstract:
Attributes of a data set to be employed in generating a predictive model are analyzed based on entropy, chi-square, or similar statistical measure. A target group of samples exhibiting one or more desired attributes is identified, then remaining attribute values for the target group are compared to corresponding attribute values for the whole sample population. A subset of all available attributes is then selected from those attributes which exhibit, when comparing attribute values of target group samples to attribute values for the whole sample population, the greatest relative difference or divergence. This subset is employed to generate the predictive model. Efficiency in generating the predictive model and the accuracy of the resulting predictive model is improved, since fewer attributes are employed and less computational resources are required.
Ben Peter Yuhas from Marietta, NY, age ~65 Get Report