Embedded bayesian network for pattern recognition
First Claim
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1. A pattern recognition method, comprising:
- forming a hierarchical statistical model using a hidden Markov model (HMM) and a coupled hidden Markov model (CHMM), the hierarchical statistical model supporting a parent layer having multiple supernodes and a child layer having multiple nodes associated with each supernode of the parent layer;
wherein either the parent layer is formed of an HMM and the child layer is formed of a CHMM, or the parent layer is formed of a CHMM and the child layer is formed of an HMM;
the hierarchical statistical model applied to two dimensional data, with the parent layer describing data in a first direction and the child layer describing data in a second direction orthogonal to the first direction;
training the hierarchical statistical model using observation vectors extracted from a data set;
obtaining an observation vector sequence from a pattern to be recognized; and
identifying the pattern by finding a substantially optimal state sequence segmentation for the hierarchical statistical model.
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Abstract
A pattern recognition procedure forms a hierarchical statistical model using a hidden Markov model and a coupled hidden Markov model. The hierarchical statistical model supports a pa 20 layer having multiple supernodes and a child layer having multiple nodes associated with each supernode of the parent layer. After training, the hierarchical statistical model uses observation vectors extracted from a data set to find a substantially optimal state sequence segmentation.
45 Citations
18 Claims
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1. A pattern recognition method, comprising:
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forming a hierarchical statistical model using a hidden Markov model (HMM) and a coupled hidden Markov model (CHMM), the hierarchical statistical model supporting a parent layer having multiple supernodes and a child layer having multiple nodes associated with each supernode of the parent layer;
wherein either the parent layer is formed of an HMM and the child layer is formed of a CHMM, or the parent layer is formed of a CHMM and the child layer is formed of an HMM;
the hierarchical statistical model applied to two dimensional data, with the parent layer describing data in a first direction and the child layer describing data in a second direction orthogonal to the first direction;training the hierarchical statistical model using observation vectors extracted from a data set; obtaining an observation vector sequence from a pattern to be recognized; and identifying the pattern by finding a substantially optimal state sequence segmentation for the hierarchical statistical model. - View Dependent Claims (2, 3, 4, 5, 6)
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7. An article comprising a computer readable storage medium having stored thereon instructions that when executed by a machine result in:
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forming a hierarchical statistical model using a hidden Markov model (HMM) and a coupled hidden Markov model (CHMM), the hierarchical statistical model supporting a parent layer having multiple supernodes and a child layer having multiple nodes associated with each supernode of the parent layer;
wherein either the parent layer is formed of an HMM and the child layer is formed of a CHMM, or the parent layer is formed of a CHMM and the child layer is formed of an HMM;
the hierarchical statistical model applied to two dimensional data, with the parent layer describing data in a first direction and the child layer describing data in a second direction orthogonal to the first direction;training the hierarchical statistical model using observation vectors extracted from a data set; obtaining an observation vector sequence from a pattern to be recognized; and identifying the pattern by finding a substantially optimal state sequence segmentation for the hierarchical statistical model. - View Dependent Claims (8, 9, 10, 11, 12)
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13. A system comprising:
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a hierarchical statistical model to use both hidden Markov models and coupled hidden Markov models to model patterns, the hierarchical statistical model supporting a parent layer having multiple supernodes and a child layer having multiple nodes associated with each supernode of the parent layer;
wherein either the parent layer is formed of a hidden Markov model (HMM) and the child layer is formed of a coupled HMM (CHMM), or the parent layer is formed of a CHMM and the child layer is formed of an HMM;
the hierarchical statistical model applied to two dimensional data, with the parent layer describing data in a first direction and the child layer describing data in a second direction orthogonal to the first direction;a training module to train for the hierarchical statistical model using observation vectors extracted from a data set; and an identification module to obtain an observation vector sequence for a pattern to be recognized, and to identify the pattern by finding a substantially optimal state sequence segmentation for the hierarchical statistical model. - View Dependent Claims (14, 15, 16, 17, 18)
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Specification