02438nam a2200181   4500001000700000100004100007101000800048200017200056210003000228215002000258300024800278330158600526606002902112606001602141606001802157606003202175710004902207391185  a20121008d2012       y0pory50      ba  aeng  aApplication of machine learning to toolmarksbRecurso eletrónicoestatistically based methods for impression pattern comparisonsfNicholas D. K. Petraco ... [et al.]  aNew Yorkced. do a.d2012  a1 CD-ROMd12 cm  aResearch report funded by the U.S. Department of Justice, Award nr. 2009-DN-BX-K041. Ficheiro de 7,37 MB em formato PDF (101 p.). Disponível também em: https://www.ncjrs.gov/pdffiles1/nij/grants/239048.pdf. Acedido a 08 de outubro de 2012.  aExecutive summary. Final technical report. I. Introduction: 1. Statement of the problem. 2. Review of relevant literature. 2.1 Introduction to toolmarks and toolmark examination. 2.2 Individualization of toolmarks. 2.3 Materials for experimentation. 2.4 Two schools of thought. 2.5 Methods and techniques of toolmark examination. 2.6 Reliability of toolmark examination. 2.7 Court decisions. 2.8 Statistics and toolmarks. 3. Rationale for the research. II. Materials and Methods: 1. Materials. 2. Methods for toolmark impression data collection. 1.1 Generating reproducible toolmark impressions. 1.2 Confocal microscope. 3. Machine learning methods for toolmark comparison. 3.1 General striated toolmark surface preprocessing and feature vector construction. 3.2 The data matrix and principal component analysis. 3.3 Canonical variate analysis. 3.4 Support vector machines. 4. Methods for error rate estimation. 4.1 Resubstitution methods. 4.2 Conformal prediction theory. III. Results: 1. Toolmark impression data collection and database. 1.1 Cartridge case striation and impression pattern collection. 1.2 Striated toolmark pattern collection. 1.3 Database and web interface. 1.4 Surface visualization and measurement software. 1.5 Profile simulator software. 1.6 R software and statistical analysis scripts. 2. Statistical Analyses. 2.1 Glock 19 Cartridge Casings. 2.2 Screwdriver striation patterns. IV. Conclusions: 1. Discussion of findings. 2. Implications for policy and practice. 3. Implications for further research. V. References. VI. Dissemination of research findings.  aMARCAÇÃO DE OBJECTOS  aBALÍSTICA  aBASE DE DADOS  aARMA DE FOGO E MUNIÇÕES  aAPPLICATION OF MACHINE LEARNING TO TOOLMARKS