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ICDM
2005
IEEE
189views Data Mining» more  ICDM 2005»
15 years 3 months ago
Integrating Hidden Markov Models and Spectral Analysis for Sensory Time Series Clustering
We present a novel approach for clustering sequences of multi-dimensional trajectory data obtained from a sensor network. The sensory time-series data present new challenges to da...
Jie Yin, Qiang Yang
ALT
2010
Springer
14 years 11 months ago
Consistency of Feature Markov Processes
We are studying long term sequence prediction (forecasting). We approach this by investigating criteria for choosing a compact useful state representation. The state is supposed t...
Peter Sunehag, Marcus Hutter
ICMLA
2008
14 years 11 months ago
Detection of Sequential Outliers Using a Variable Length Markov Model
Mining for outliers in sequential databases is crucial to forward appropriate analysis of data. Therefore, many approaches for the discovery of such anomalies have been proposed. ...
Cécile Low-Kam, Anne Laurent, Maguelonne Te...
61
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BMCBI
2008
170views more  BMCBI 2008»
14 years 9 months ago
Implementing EM and Viterbi algorithms for Hidden Markov Model in linear memory
Background: The Baum-Welch learning procedure for Hidden Markov Models (HMMs) provides a powerful tool for tailoring HMM topologies to data for use in knowledge discovery and clus...
Alexander G. Churbanov, Stephen Winters-Hilt
MICCAI
2001
Springer
15 years 2 months ago
Segmentation of Dynamic N-D Data Sets via Graph Cuts Using Markov Models
Abstract. This paper describes a new segmentation technique for multidimensional dynamic data. One example of such data is a perfusion sequence where a number of 3D MRI volumes sho...
Yuri Boykov, Vivian S. Lee, Henry Rusinek, Ravi Ba...