'False positive rate' presentation slideshows

Ch. 6: Face detection

Ch. 6: Face detection. KH Wong. Introduction. Face interface Face detection Face recognition. Face database. Output:. Mr.Chan Prof..Cheng. Face detection. Face recognition. Face detection [1]. To detect faces in an image (Not recognize it yet) Challenges

1.29k views • 62 slides

DIAGNOSTIC TESTS

776 views • 60 slides

Type 1 Error and Power Calculation for Association Analysis

Type 1 Error and Power Calculation for Association Analysis. Pak Sham & Shaun Purcell Advanced Workshop Boulder, CO, 2005. Statistical Tests. Standard test theory Type 1: Rejecting the null hypothesis when it is true ( ? ).

568 views • 41 slides

Computational Technologies for Algorithmic Medicine

M Sriram Iyengar, PhD Asst. Professor, School of Health Information Sciences, University of Texas Health Science Center at Houston Informatics Research Scientist, Medical Informatics and Health Care Systems, NASA Johnson Space Center, Houston, TX John R Svirbely, MD TriHealth, Cincinnati, Ohio.

642 views • 41 slides

Feature Ranking and Selection for Intrusion Detection using Support Vector Machines

Feature Ranking and Selection for Intrusion Detection using Support Vector Machines. Srinivas Mukkamala & Andrew H. Sung Computer Science Department New Mexico Tech. Intrusion Data. Raw TCP/IP dump data collected form a network by simulating a typical U.S. Air Force LAN .

570 views • 38 slides

Using Failure Information Analysis to Detect Enterprise Zombies

Using Failure Information Analysis to Detect Enterprise Zombies. Zhaosheng Zhu, Vinod Yegneswaran, Yan Chen Lab of Internet and Security Technology Northwestern University SRI International. Motivation. Increasing prevalence and sophistication of malware

456 views • 33 slides

Bloom Filters

Bloom Filters. Benoit Donnet November 30th, 2006. 1. Context. Introduced in 1970 ([bloom]) Set membership problem Trade-off between space and computing complexity Lossy summary technique Historical usage Spell checking ([McIlroy]) Database ([Bratbergsengen]). Content. Bloom filters

493 views • 33 slides

Defending Distributed Systems Against Malicious Intrusions and Network Anomalies

Defending Distributed Systems Against Malicious Intrusions and Network Anomalies. Kai Hwang Internet and Grid Computing Laboratory University of Southern California

447 views • 32 slides

First and Second Trimester Trisomy Screening

First and Second Trimester Trisomy Screening. J. Christopher Glantz. Trisomies 21, 18, and 13. Incidence (live births) 21: 1/6-800 18: 1/7,000 13: <1/10,000 Trimester Frequency: First>Second>Third Spontaneous and induced losses 20-30% for 21, 40% for 13, 66% for 18

663 views • 31 slides

STATISTICAL EVALUATION OF DIAGNOSTIC TESTS

STATISTICAL EVALUATION OF DIAGNOSTIC TESTS. Describing the performance of a new diagnostic test.

1.48k views • 29 slides

Ladislav Pecen

False Discovery Rate (FDR) = proportion of false positive results out of all positive results (positive result = statistically significant result). Ladislav Pecen. Outline. Introduction Familywise Error Rate False Discovery Rate Positive False Discovery Rate Comparison of approaches

508 views • 28 slides

BotGraph: Large Scale Spamming Botnet Detection

BotGraph: Large Scale Spamming Botnet Detection. Yao Zhao, Yinglian Xie, Fang Yu, Qifa Ke, Yuan Yu, Yan Chen, and Eliot Gillum. Speaker: ???. References.

389 views • 26 slides

Automated Chip QC

Automated Chip QC. Michael Elashoff. Chip QC. Transition from mostly manual/visual chip QC to mostly automated chip QC Database of passing and failing chips to serve as the training set (5K passing, 2K failing). Chip QC: Defect Classes. In order of occurrence: Dimness High Background

433 views • 25 slides

(Alignment) Score Statistics

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Detecting Duplicates over Sliding Windows with RAM-Efficient Detached Counting Bloom Filter Arrays

330 views • 22 slides

Bug Driven Bug Finding

Bug Driven Bug Finding. Chadd Williams. Motivation. Finding bugs in software is important Statically checking code has been effective finds complex errors no need to run the code Many static checkers available some with specific bug patterns to find

349 views • 21 slides

From W1-S16

301 views • 20 slides

Filtering Spam With

Filtering Spam With. Justin Mason, SpamAssassin Project & Deersoft http://SpamAssassin.org/. What Is Spam?. Best description: "Unsolicited Bulk E-mail" In human terms: bulk e-mail you didn't want, and didn't ask for

363 views • 20 slides

Lester L. Yuan 1 Jeroen Gerritsen 2 James B. Stribling 2

Lester L. Yuan 1 Jeroen Gerritsen 2 James B. Stribling 2 1 Office of Research and Development, US EPA, Washington, DC 2 TetraTech, Inc., Owings Mill, MD. How often are we wrong? A Bayesian assessment of taxonomic identifications for the National Wadeable Streams Assessment. Basic Question.

259 views • 18 slides

Supplemental Figure 1. ROC plot of CD177 expression as a predictor of GI irAE

Supplemental Figure 1. ROC plot of CD177 expression as a predictor of GI irAE. 1.0. 0.8. 0.6. True positive rate. 0.4. 0.2. 0.0. 0.2. 0.4. 0.6. 0.8. 1.0. 0.0. False positive rate.

121 views • 1 slides