• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 53 matches

• 2021
• 
  Sagar Venkatesh Gubbi, Bharadwaj Amrutur:
  Scene Text Detection for Augmented Reality - Character Bigram Approach to reduce False Positive Rate. CoRR abs/2101.01054 (2021)
• 2020
• 
  Majd Latah, Levent Toker:
  Minimizing false positive rate for DoS attack detection: A hybrid SDN-based approach. ICT Express 6(2): 125-127 (2020)
• 
  Changhee Han, Euijin Kim, Chang-Hwan Im:
  Development of a Brain-Computer Interface Toggle Switch with Low False-Positive Rate Using Respiration-Modulated Photoplethysmography. Sensors 20(2): 348 (2020)
• 
  Baptiste Couvy-Duchesne, Futao Zhang, Kathryn E. Kemper, Julia Sidorenko, Naomi R. Wray, Peter M. Visscher, Olivier Colliot, Jian Yang:
  Linear Mixed Models Minimise False Positive Rate and Enhance Precision of Mass Univariate Vertex-Wise Analyses of Grey-Matter. ISBI 2020: 404-407
• 
  Zhe Guo, Ruiyao Zhang, Qin Li, Xinkai Liu, Daiki Nemoto, Kazutomo Togashi, S. M. Isuru Niroshana, Yuchen Shi, Xin Zhu:
  Reduce False-Positive Rate by Active Learning for Automatic Polyp Detection in Colonoscopy Videos. ISBI 2020: 1655-1658
• 
  Pingfan Wang, Nanlin Jin, Gerhard Fehringer:
  Concept drift detection with False Positive rate for multi-label classification in IoT data stream. UCET 2020: 1-4
• 
  Robert Long:
  Fairness in machine learning: against false positive rate equality as a measure of fairness. CoRR abs/2007.02890 (2020)
• 
  Jesse Russell:
  Machine Learning Fairness in Justice Systems: Base Rates, False Positives, and False Negatives. CoRR abs/2008.02214 (2020)
• 2019
• 
  Shuchen Kong, Weiwei Shen, Yingbin Zheng, Ao Zhang, Jian Pu, Jun Wang:
  False positive rate control for positive unlabeled learning. Neurocomputing 367: 13-19 (2019)
• 
  Fatma Hachmi, Khadouja Boujenfa, Mohamed Limam:
  Enhancing the Accuracy of Intrusion Detection Systems by Reducing the Rates of False Positives and False Negatives Through Multi-objective Optimization. J. Netw. Syst. Manag. 27(1): 93-120 (2019)
• 
  George Mamalakis, Christos Diou, Andreas L. Symeonidis, Leonidas Georgiadis:
  Of daemons and men: reducing false positive rate in intrusion detection systems with file system footprint analysis. Neural Comput. Appl. 31(11): 7755-7767 (2019)
• 
  Yuma Koizumi, Shin Murata, Noboru Harada, Shoichiro Saito, Hisashi Uematsu:
  SNIPER: Few-shot Learning for Anomaly Detection to Minimize False-negative Rate with Ensured True-positive Rate. ICASSP 2019: 915-919
• 
  Anthony Kleerekoper, Andrew Schofield:
  The False-Positive Rate of Automated Plagiarism Detection for SQL Assessments. UKICER 2019: 6:1-6:6
• 2018
• 
  Rupert Faltermeier, Martin A. Proescholdt, Stefan Wolf, Sylvia Bele, Alexander Brawanski:
  A Patient-Independent Significance Test by Means of False-Positive Rates in Selected Correlation Analysis of Brain Multimodal Monitoring Data. Comput. Math. Methods Medicine 2018: 6821893:1-6821893:8 (2018)
• 
  Douglas N. Greve, Bruce Fischl:
  False positive rates in surface-based anatomical analysis. NeuroImage 171: 6-14 (2018)
• 
  Martin Steinebach, Andre Ester, Huajian Liu, Sascha Zmuzinksi:
  Double Embedding Steganalysis: Steganalysis with Low False Positive Rate. MPS@CCS 2018: 38-47
• 
  Jin-Xiong Lv, Shikui Tu:
  Integration of Data-Space and Statistics-Space Boundary-Based Test to Control the False Positive Rate. ICIC (3) 2018: 628-638
• 
  Hayato Sasaki, Masaya Nakata, Mizuki Yamamoto, Teppei Takeshima, Yasushi Yumura, Tomoki Hamagami:
  Investigation about Control of False Positive Rate for Automatic Sperm Detection in Assisted Reproductive Technology. SMC 2018: 1964-1969
• 
  Sheng Chen, Jia Guo, Yang Liu, Xiang Gao, Zhen Han:
  Global Norm-Aware Pooling for Pose-Robust Face Recognition at Low False Positive Rate. CoRR abs/1808.00435 (2018)
• 2017
• 
  Robert W. Cox, Gang Chen, Daniel R. Glen, Richard C. Reynolds, Paul A. Taylor:
  FMRI Clustering in AFNI: False-Positive Rates Redux. Brain Connect. 7(3): 152-171 (2017)
• 
  Lawrence L. Wald, Jonathan R. Polimeni:
  Impacting the effect of fMRI noise through hardware and acquisition choices - Implications for controlling false positive rates. NeuroImage 154: 15-22 (2017)
• 2016
• 
  Anders E. Bilgrau, Steffen Falgreen, Anders Petersen, Malene Krag Kjeldsen, Julie Støve Bødker, Hans Erik Johnsen, Karen Dybkær, Martin Bøgsted:
  Unaccounted uncertainty from qPCR efficiency estimates entails uncontrolled false positive rates. BMC Bioinform. 17: 159 (2016)
• 
  Jinggong Xiang-Yu, Zongfeng Yang, Kun Tang, Haipeng Li:
  Revisiting the false positive rate in detecting recent positive selection. Quant. Biol. 4(3): 207-216 (2016)
• 2015
• 
  José Gómez-Torrecillas, F. J. Lobillo, Gabriel Navarro:
  Information-bit error rate and false positives in an MDS code. Adv. Math. Commun. 9(2): 149-168 (2015)
• 2014
• 
  Jie Fang, Hongjia Ouyang, Liangzhong Shen, Edward R. Dougherty, Wenbin Liu:
  Using the minimum description length principle to reduce the rate of false positives of best-fit algorithms. EURASIP J. Bioinform. Syst. Biol. 2014: 13 (2014)
• 
  Jihong Kim:
  On the False Positive Rate of the Bloom Filter in Case of Using Multiple Hash Functions. AsiaJCIS 2014: 26-30
• 
  Ognjen Arandjelovic:
  A framework for improving the performance of verification algorithms with a low false positive rate requirement and limited training data. IJCB 2014: 1-7
• 
  Ognjen Arandjelovic:
  A framework for improving the performance of verification algorithms with a low false positive rate requirement and limited training data. CoRR abs/1406.7360 (2014)
• 
  Ender Konukoglu, Melanie Ganz:
  Approximate False Positive Rate Control in Selection Frequency for Random Forest. CoRR abs/1410.2838 (2014)
• 2013
• 
  Omed S. Khalind, Julio C. Hernandez-Castro, Benjamin Aziz:
  A study on the false positive rate of Stegdetect. Digit. Investig. 9(3-4): 235-245 (2013)

skipping 23 more matches