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» Stochastic Motif Extraction Using Hidden Markov Model
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ICDM
2006
IEEE
137views Data Mining» more  ICDM 2006»
15 years 5 months ago
Mining Complex Time-Series Data by Learning Markovian Models
In this paper, we propose a novel and general approach for time-series data mining. As an alternative to traditional ways of designing specific algorithm to mine certain kind of ...
Yi Wang, Lizhu Zhou, Jianhua Feng, Jianyong Wang, ...
ICASSP
2011
IEEE
14 years 3 months ago
Destination-aware target tracking via syntactic signal processing
We consider the prediction of a target’s destination and simultaneously recover its filtered trajectory. Two novel models for trajectories with known destinations are presented...
Mustafa Fanaswala, Vikram Krishnamurthy, Langford ...
RIAO
2000
15 years 1 months ago
Learning for Sequence Extraction Tasks
We consider the application of machine learning techniques for sequence modeling to Information Retrieval (IR) and surface Information Extraction (IE) tasks. We introduce a generi...
Massih-Reza Amini, Hugo Zaragoza, Patrick Gallinar...
BMCBI
2007
142views more  BMCBI 2007»
14 years 12 months ago
Improving model construction of profile HMMs for remote homology detection through structural alignment
Background: Remote homology detection is a challenging problem in Bioinformatics. Arguably, profile Hidden Markov Models (pHMMs) are one of the most successful approaches in addre...
Juliana S. Bernardes, Alberto M. R. Dávila,...
MASCOTS
2003
15 years 1 months ago
Derivation of Passage-time Densities in PEPA Models using ipc: the Imperial PEPA Compiler
We present a technique for defining and extracting passage-time densities from high-level stochastic process algebra models. Our high-level formalism is PEPA, a popular Markovian...
Jeremy T. Bradley, Nicholas J. Dingle, Stephen T. ...