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03388nam a22005655i 4500 |
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978-0-387-77627-9 |
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DE-He213 |
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20151204170056.0 |
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cr nn 008mamaa |
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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 COM016000
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|a 570.15195
|2 23
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|a Biometric System and Data Analysis
|h [electronic resource] :
|b Design, Evaluation, and Data Mining /
|c edited by Ted Dunstone, Neil Yager.
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| 264 |
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1 |
|a Boston, MA :
|b Springer US,
|c 2009.
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| 300 |
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|a XX, 268 p.
|b online resource.
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| 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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|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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0 |
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|a An Overview of Biometrics -- An Introduction to Biometric Data Analysis -- Biometric Matching Basics -- Biometric Data -- Multimodal Systems -- Performance Testing and Reporting -- Definitions -- The Biometric Performance Hierarchy -- System Evaluation: The Statistical Basis of Biometric Systems -- Individual Evaluation: The Biometric Menagerie -- Group Evaluation: Data Mining for Biometrics -- Special Topics in Biometric Data Analysis -- Proof of Identity -- Covert Surveillance Systems -- Vulnerabilities.
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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 |
|
0 |
|a Data mining.
|
| 650 |
|
0 |
|a Artificial intelligence.
|
| 650 |
|
0 |
|a Computer graphics.
|
| 650 |
|
0 |
|a Biometrics (Biology).
|
| 650 |
|
0 |
|a Applied mathematics.
|
| 650 |
|
0 |
|a Engineering mathematics.
|
| 650 |
|
0 |
|a Probabilities.
|
| 650 |
1 |
4 |
|a Computer Science.
|
| 650 |
2 |
4 |
|a Biometrics.
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| 650 |
2 |
4 |
|a Probability Theory and Stochastic Processes.
|
| 650 |
2 |
4 |
|a Applications of Mathematics.
|
| 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).
|
| 700 |
1 |
|
|a Dunstone, Ted.
|e editor.
|
| 700 |
1 |
|
|a Yager, Neil.
|e editor.
|
| 710 |
2 |
|
|a SpringerLink (Online service)
|
| 773 |
0 |
|
|t Springer eBooks
|
| 776 |
0 |
8 |
|i Printed edition:
|z 9780387776255
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1007/978-0-387-77627-9
|z Full Text via HEAL-Link
|
| 912 |
|
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|a ZDB-2-SCS
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| 950 |
|
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|a Computer Science (Springer-11645)
|
