Multiple Self-Organizing Maps for Intrusion Detection Brandon Craig Rhodes, James A. Mahaffey, James D. Cannady fbrandon.rhodes,jim.mahaffey,[removed] Computer Science and Information Technology Div

Source URL: csrc.nist.gov

• Cybernetics
• Self-organizing map
• Anomaly detection
• Supervised learning
• Intrusion detection system
• Feature
• Learning Vector Quantization
• Hybrid Kohonen SOM
• Neural networks
• Statistics
• Machine learning

Using Lightweight Media Forensics

Source URL: www.dhs.gov

• Data security
• Anomaly detection
• Cluster analysis
• Outlier
• Statistics
• Data analysis
• Data mining

Table of Contents 1 Upkeep..................................................................................................................................................................................................

Source URL: marswiki.jrc.ec.europa.eu

• Anomaly
• Land lot
• Physics
• Knowledge
• Real estate
• Land use
• Land-parcel identification system
• Anomaly detection

Scene in the Loop: Towards Adaptation-by-Tracking in RGB-D Data Luciano Spinello, Cyrill Stachniss and Wolfram Burgard University of Freiburg, Germany Abstract—This paper addresses the problem of adapting an existing o

Source URL: www.first-mm.eu

• Anomaly detection
• Data security
• Computer vision
• Outlier
• Support vector machine
• RGB color model
• Statistics
• Data mining
• Statistical classification

Scene in the Loop: Towards Adaptation-by-Tracking in RGB-D Data Luciano Spinello, Cyrill Stachniss and Wolfram Burgard University of Freiburg, Germany Abstract—This paper addresses the problem of adapting an existing o

Source URL: first-mm.informatik.uni-freiburg.de

• Anomaly detection
• Data security
• Computer vision
• Outlier
• Support vector machine
• RGB color model
• Statistics
• Data mining
• Statistical classification

Scene in the Loop: Towards Adaptation-by-Tracking in RGB-D Data Luciano Spinello, Cyrill Stachniss and Wolfram Burgard University of Freiburg, Germany Abstract—This paper addresses the problem of adapting an existing o

Source URL: tapas.informatik.uni-freiburg.de

• Anomaly detection
• Data security
• Computer vision
• Outlier
• Support vector machine
• RGB color model
• Statistics
• Data mining
• Statistical classification

Scene in the Loop: Towards Adaptation-by-Tracking in RGB-D Data Luciano Spinello, Cyrill Stachniss and Wolfram Burgard University of Freiburg, Germany Abstract—This paper addresses the problem of adapting an existing o

Source URL: www.tapas-project.eu

• Anomaly detection
• Data security
• Computer vision
• Outlier
• Support vector machine
• RGB color model
• Statistics
• Data mining
• Statistical classification

OddBall: Spotting Anomalies in Weighted Graphs Leman Akoglu Mary McGlohon Christos Faloutsos

Source URL: www.cs.cmu.edu

• Graph
• Bipartite graph
• Adjacency matrix
• Directed graph
• Anomaly detection
• Signed graph
• Graph theory
• Mathematics
• Algebraic graph theory

Improved Non-invasive Lung Cancer Screening using Kernel-based Anomaly Detection” Samuel Cheng, PhD Abstract RX and kernel-based RX algorithms will be developed for the cancer nodule screening. For efficient prototypin

Source URL: www.ouhsc.edu

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 18, NO. 5, SEPTEMBER[removed]System Light-Loading Technology for mHealth: Manifold-Learning-Based Medical Data Cleansing

Source URL: vast.cs.ucla.edu

• Medical informatics
• Health informatics
• Medical monitor
• Telemedicine
• MHealth
• Electrocardiography
• Body area network
• Anomaly detection
• Wireless Health
• Medicine
• Health
• Telehealth