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ICML
2001
IEEE
15 years 10 months ago
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hid...
John D. Lafferty, Andrew McCallum, Fernando C. N. ...
CVPR
1999
IEEE
15 years 12 months ago
Time-Series Classification Using Mixed-State Dynamic Bayesian Networks
We present a novel mixed-state dynamic Bayesian network (DBN) framework for modeling and classifying timeseries data such as object trajectories. A hidden Markov model (HMM) of di...
Vladimir Pavlovic, Brendan J. Frey, Thomas S. Huan...
GFKL
2007
Springer
184views Data Mining» more  GFKL 2007»
15 years 4 months ago
A Probabilistic Relational Model for Characterizing Situations in Dynamic Multi-Agent Systems
Abstract. Artificial systems with a high degree of autonomy require reliable semantic information about the context they operate in. State interpretation, however, is a difficult ...
Daniel Meyer-Delius, Christian Plagemann, Georg vo...
ICASSP
2009
IEEE
15 years 4 months ago
Combining mixture weight pruning and quantization for small-footprint speech recognition
Semi-continuous acoustic models, where the output distributions for all Hidden Markov Model states share a common codebook of Gaussian density functions, are a well-known and prov...
David Huggins-Daines, Alexander I. Rudnicky
WSDM
2010
ACM
322views Data Mining» more  WSDM 2010»
15 years 7 months ago
Inferring Search Behaviors Using Partially Observable Markov (POM) Model
This article describes an application of the partially observable Markov (POM) model to the analysis of a large scale commercial web search log. Mathematically, POM is a variant o...
Kuansan Wang, Nikolas Gloy, Xiaolong Li