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» Activity rhythm detection and modeling
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ICMCS
2009
IEEE
115views Multimedia» more  ICMCS 2009»
13 years 7 months ago
A framework to detect and classify activity transitions in low-power applications
Minimizing the number of computations a low-power device makes is important to achieve long battery life. In this paper we present a framework for a low-power device to minimize t...
Jeffrey Boyd, Hari Sundaram
TKDE
2008
134views more  TKDE 2008»
13 years 9 months ago
Sensor-Based Abnormal Human-Activity Detection
With the availability of affordable sensors and sensor networks, sensor-based human-activity recognition has attracted much attention in artificial intelligence and ubiquitous comp...
Jie Yin, Qiang Yang, Jeffrey Junfeng Pan
CISS
2008
IEEE
14 years 4 months ago
Unsupervised distributional anomaly detection for a self-diagnostic speech activity detector
— One feature that classification algorithms typically lack is the ability to know what they do not know. With this knowledge an algorithm would be able to operate in any domain...
Nash M. Borges, Gerard G. L. Meyer
NIPS
2004
13 years 11 months ago
Active Learning for Anomaly and Rare-Category Detection
We introduce a novel active-learning scenario in which a user wants to work with a learning algorithm to identify useful anomalies. These are distinguished from the traditional st...
Dan Pelleg, Andrew W. Moore

Publication
226views
12 years 8 months ago
Modelling Multi-object Activity by Gaussian Processes
We present a new approach for activity modelling and anomaly detection based on non-parametric Gaussian Process (GP) models. Specifically, GP regression models are formulated to l...
Chen Change Loy, Tao Xiang, Shaogang Gong