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100301s2009 xxu| s |||| 0|eng d |
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|a 9780387776279
|9 978-0-387-77627-9
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|a 10.1007/978-0-387-77627-9
|2 doi
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|a QH323.5
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|a 570.15195
|2 23
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| 100 |
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|a Dunstone, Ted.
|e editor.
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| 245 |
1 |
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|a Biometric System and Data Analysis
|b Design, Evaluation, and Data Mining /
|c edited by Ted Dunstone, Neil Yager.
|h [electronic resource] :
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| 264 |
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1 |
|a Boston, MA :
|b Springer US,
|c 2009.
|
| 300 |
# |
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|b online resource.
|
| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 347 |
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|a text file
|b PDF
|2 rda
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|a Preface -- Preliminaries -- Biometric Evaluation Fundamentals -- Biometric Data -- Performance evaluation Hierarchy -- System Evaluation: The Statistical Basis for Biometric Systems -- Verification -- Identification (open vs closed) -- Enrollment Measures -- Individual Evaluation: The Biometric Menagerie -- Group Evaluation: Data Mining for Biometrics -- Special Topics in Biometric Evaluations -- System Setup and Optimization -- The Evaluation of Systems in Production -- Evaluating Surveillance Systems -- Conclusion -- References -- Index.
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| 520 |
# |
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|a Biometric System and Data Analysis: Design, Evaluation, and Data Mining brings together aspects of statistics and machine learning to provide a comprehensive guide to evaluate, interpret and understand biometric data. This professional book naturally leads to topics including data mining and prediction, widely applied to other fields but not rigorously to biometrics. This volume places an emphasis on the various performance measures available for biometric systems, what they mean, and when they should and should not be applied. The evaluation techniques are presented rigorously, however are always accompanied by intuitive explanations that convey the essence of the statistical concepts in a general manner. Designed for a professional audience composed of practitioners and researchers in industry, Biometric System and Data Analysis: Design, Evaluation, and Data Mining is also suitable as a reference for advanced-level students in computer science and engineering.
|
| 650 |
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0 |
|a Computer science.
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| 650 |
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|a Data encryption (Computer science).
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| 650 |
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0 |
|a Database management.
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| 650 |
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0 |
|a Data mining.
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| 650 |
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0 |
|a Artificial intelligence.
|
| 650 |
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0 |
|a Computer vision.
|
| 650 |
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0 |
|a Biometrics.
|
| 650 |
1 |
4 |
|a Computer Science.
|
| 650 |
2 |
4 |
|a Biometrics.
|
| 650 |
2 |
4 |
|a Computer Imaging, Vision, Pattern Recognition and Graphics.
|
| 650 |
2 |
4 |
|a Data Mining and Knowledge Discovery.
|
| 650 |
2 |
4 |
|a Artificial Intelligence (incl. Robotics).
|
| 650 |
2 |
4 |
|a Data Encryption.
|
| 650 |
2 |
4 |
|a Database Management.
|
| 700 |
1 |
# |
|a Yager, Neil.
|e editor.
|
| 710 |
2 |
# |
|a SpringerLink (Online service)
|
| 773 |
0 |
# |
|t Springer eBooks
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| 776 |
0 |
8 |
|i Printed edition:
|z 9780387776255
|
| 856 |
4 |
0 |
|u https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-0-387-77627-9
|
| 912 |
# |
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|a ZDB-2-SCS
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| 950 |
# |
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|a Computer Science (Springer-11645)
|