Application of Machine Learning to Toolmarks: Statistically Based Methods for Impression Pattern Comparisons
The author(s) shown below used Federal funds provided by the U.S. Department of Justice and prepared the following final report: Document Title: Application of Machine Learning to Toolmarks: Statistically Based Methods for Impression Pattern Comparisons Author: Nicholas D. K. Petraco, Ph.D.; Helen Chan, B.A.; Peter R. De Forest, D.Crim.; Peter Diaczuk, M.S.; Carol Gambino, M.S., James Hamby, Ph.D.; Frani L. Kammerman, M.S.; Brooke W. Kammrath, M.A., M.S; Thomas A. Kubic, M.S., J.D., Ph.D.; Loretta Kuo, M.S.; Patrick McLaughlin; Gerard Petillo, B.A.; Nicholas Petraco, M.S.; Elizabeth W. Phelps, M.S.; Peter A. Pizzola, Ph.D.; Dale K. Purcell, M.S.; Peter Shenkin, Ph.D. Document No.: 239048 Date Received: July 2012 Award Number: 2009-DN-BX-K041 This report has not been published by the U.S. Department of Justice. To provide better customer service, NCJRS has made this Federally- funded grant final report available electronically in addition to traditional paper copies. Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the U.S. Department of Justice. This document is a research report submitted to the U.S. Department of Justice. This report has not been published by the Department. Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the U.S. Department of Justice. Report Title: Application of Machine Learning to Toolmarks: Statistically Based Methods for Impression Pattern Comparisons Award Number: 2009-DN-BX-K041 Authors: Nicholas D.
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