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Anomaly detection
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#	Item
321	Office for National Statistics EUREDIT Project - Deliverables EUREDIT PROJECT - DELIVERABLES TABLE 1 – STANDARD METHODS Add to Reading List Source URL: www.cs.york.ac.uk Language: English - Date: 2003-04-03 07:19:12 Missing data Imputation Regression analysis Econometrics Robust regression Outlier Anomaly detection Statistics Data analysis Robust statistics
322	Advanced Analytics for Zero Defects GSA – Quality Working Group Meeting March 25, 2015 Anil Gandhi / Data Scientist Joy Gandhi / Quality Consultant Add to Reading List Source URL: www.gsaglobal.org Language: English - Date: 2015-03-25 21:07:28 Formal sciences Anomaly detection Data mining Data security Unsupervised learning Supervised learning Analytics Semiconductor Machine learning Statistics Business intelligence
323	New Approaches to monitoring for biosecurity risk David R. Fox What is risk? 1. risk = hazard x exposure Add to Reading List Source URL: www.acera.unimelb.edu.au Language: English - Date: 2012-04-04 21:47:35 Control chart Product management Quality Standard deviation Anomaly detection Estimation theory Anomaly Outlier Statistics Data analysis Change detection
324	Detecting Anomalies in Data Streams using Statecharts Vasile-Marian Scuturici1, Dan-Mircea Suciu2, Romain Vuillemot1, Aris Ouksel3, Lionel Brunie1 INSA Lyon Babes-Bolyai University, Cluj-Napoca 3 Add to Reading List Source URL: romain.vuillemot.net Language: English - Date: 2015-01-18 16:53:53 UML diagrams Computer networking Databases Datasource State diagram Information Dataspaces Timestamp Anomaly detection Computing Information science Models of computation
325	IVS: Outlier detection and treatment Add to Reading List Source URL: www.med.govt.nz Language: English - Date: 2015-02-19 17:00:38 Data analysis Outlier Data mining Gross domestic product Quartile Anomaly detection Statistics Robust statistics National accounts
326	Methods and Applications for Distance Based ANN Training Christoph Lassner, Rainer Lienhart Multimedia Computing and Computer Vision Lab Augsburg University, Universit¨atsstr. 6a, 86159 Augsburg, Germany Email: {christo Add to Reading List Source URL: www.multimedia-computing.de Language: English - Date: 2013-12-13 13:30:03 Statistical classification Computational statistics Neural networks Support vector machine Dimension reduction Artificial neural network Anomaly detection Principal component analysis Pattern recognition Statistics Machine learning Multivariate statistics
327	LSDA: Large Scale Detection through Adaptation Judy Hoffman , Sergio Guadarrama , Eric Tzeng , Ronghang Hu∇ , Jeff Donahue , EECS, UC Berkeley, ∇ EE, Tsinghua University {jhoffman, sguada, tzeng, jdonahue}@eec Add to Reading List Source URL: www.cs.berkeley.edu Language: English - Date: 2014-11-01 19:43:19 Anomaly detection Data mining Data security Statistical classification Linear classifier Segmentation Boosting methods for object categorization Statistics Image processing Artificial intelligence
328	Technische Universit¨ at Mu ¨ nchen Fakult¨ at f¨ ur Informatik Add to Reading List Source URL: www.net.in.tum.de Language: English - Date: 2015-01-12 11:06:50 Data mining Data security Computing Peer-to-peer Social peer-to-peer processes Anomaly GNUnet ANts P2P Software Statistics Anomaly detection
329	The Base-Rate Fallacy and its Implications for the Difficulty of Intrusion Detection Stefan Axelsson Department of Computer Engineering Chalmers University of Technology Goteborg, Add to Reading List Source URL: www.raid-symposium.org Language: English - Date: 2002-04-05 12:17:29 Data mining Data security Spam filtering Intrusion detection system Burglar alarm Base rate fallacy Receiver operating characteristic Type I and type II errors Anomaly detection Statistics Security Safety
330	Volume 3, Issue 1, January – December[removed]Machine Learning Methods for Computer Security (Dagstuhl Perspectives Workshop[removed]Anthony D. Joseph, Pavel Laskov, Fabio Roli, J. Doug Tygar, and Blaine Nelson . Add to Reading List Source URL: drops.dagstuhl.de Language: English - Date: 2014-10-15 03:25:19 Machine learning Data security National security Computing Cyberwarfare Malware Anomaly detection Computational learning theory Antivirus software Security Computer security Computer network security

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