Tools
Sorted by:
Try your query at:
 |
 |
 |
 |
 |
 |
Results 1 - 10
of
35,019
The Infinite Hidden Markov Model
- Machine Learning
, 2002
"... We show that it is possible to extend hidden Markov models to have a countably infinite number of hidden states. By using the theory of Dirichlet processes we can implicitly integrate out the infinitely many transition parameters, leaving only three hyperparameters which can be learned from data. Th ..."
Abstract
-
Cited by 637 (41 self)
- Add to MetaCart
We show that it is possible to extend hidden Markov models to have a countably infinite number of hidden states. By using the theory of Dirichlet processes we can implicitly integrate out the infinitely many transition parameters, leaving only three hyperparameters which can be learned from data
An introduction to hidden Markov models
- IEEE ASSp Magazine
, 1986
"... The basic theory of Markov chains has been known to ..."
Abstract
-
Cited by 1132 (2 self)
- Add to MetaCart
The basic theory of Markov chains has been known to
What is a hidden Markov model?
, 2004
"... Often, problems in biological sequence analysis are just a matter of putting the right label on each residue. In gene identification, we want to label nucleotides as exons, introns, or intergenic sequence. In sequence alignment, we want to associate residues in a query sequence with ho-mologous resi ..."
Abstract
-
Cited by 1344 (8 self)
- Add to MetaCart
, and a polyadenylation signal. All too often, piling more reality onto a fragile ad hoc program makes it collapse under its own weight. Hidden Markov models (HMMs) are a formal foundation for making probabilistic models of
Coupled hidden Markov models for complex action recognition
, 1996
"... We present algorithms for coupling and training hidden Markov models (HMMs) to model interacting processes, and demonstrate their superiority to conventional HMMs in a vision task classifying two-handed actions. HMMs are perhaps the most successful framework in perceptual computing for modeling and ..."
Abstract
-
Cited by 501 (22 self)
- Add to MetaCart
We present algorithms for coupling and training hidden Markov models (HMMs) to model interacting processes, and demonstrate their superiority to conventional HMMs in a vision task classifying two-handed actions. HMMs are perhaps the most successful framework in perceptual computing for modeling
Hidden Markov Models and Analysis of
"... Hidden Markov models are widely used in the areas of speech recognition and bioinformatics. Hidden Markov models differ from simple Markov models by including hidden states in addition to observable states. For example in bioinformatics, ..."
Abstract
- Add to MetaCart
Hidden Markov models are widely used in the areas of speech recognition and bioinformatics. Hidden Markov models differ from simple Markov models by including hidden states in addition to observable states. For example in bioinformatics,
Hidden Markov models for detecting remote protein homologies
- Bioinformatics
, 1998
"... A new hidden Markov model method (SAM-T98) for nding remote homologs of protein sequences is described and evaluated. The method begins with a single target sequence and iteratively builds a hidden Markov model (hmm) from the sequence and homologs found using the hmm for database search. SAM-T98 is ..."
Abstract
-
Cited by 462 (15 self)
- Add to MetaCart
A new hidden Markov model method (SAM-T98) for nding remote homologs of protein sequences is described and evaluated. The method begins with a single target sequence and iteratively builds a hidden Markov model (hmm) from the sequence and homologs found using the hmm for database search. SAM-T98
Measures on Hidden Markov Models
, 1999
"... Hidden Markov models were introduced in the beginning of the 1970's as a tool in speech recognition. During the last decade they have been found useful in addressing problems in computational biology such as characterising sequence families, gene finding, structure prediction and phylogenetic a ..."
Abstract
-
Cited by 5 (1 self)
- Add to MetaCart
Hidden Markov models were introduced in the beginning of the 1970's as a tool in speech recognition. During the last decade they have been found useful in addressing problems in computational biology such as characterising sequence families, gene finding, structure prediction and phylogenetic
Measures on hidden Markov models
"... Abstract Hidden Markov models were introduced in the beginning of the 1970's as a tool in speech recognition. During the last decade they have been found useful in addressing problems in computational biology such as characterising sequence families, gene finding, structure prediction and phylo ..."
Abstract
- Add to MetaCart
Abstract Hidden Markov models were introduced in the beginning of the 1970's as a tool in speech recognition. During the last decade they have been found useful in addressing problems in computational biology such as characterising sequence families, gene finding, structure prediction
Profile Hidden Markov Models
"... In the previous lecture, we began our discussion of profiles, and today we will talk about how to use hidden Markov models to build profiles. One of the advantages of using hidden Markov models for profile analysis is that they provide a better method for dealing with gaps found in protein families. ..."
Abstract
- Add to MetaCart
In the previous lecture, we began our discussion of profiles, and today we will talk about how to use hidden Markov models to build profiles. One of the advantages of using hidden Markov models for profile analysis is that they provide a better method for dealing with gaps found in protein families
Hidden Markov Models
, 2007
"... A hidden Markov model (HMM) is a temporal probabilistic model in which the state of the process is described by a single discrete random variable. Loosely speaking, it is a Markov chain observed in noise. The theory of hidden Markov models was developed in the late 1960s and early 1970s by Baum, Eag ..."
Abstract
- Add to MetaCart
A hidden Markov model (HMM) is a temporal probabilistic model in which the state of the process is described by a single discrete random variable. Loosely speaking, it is a Markov chain observed in noise. The theory of hidden Markov models was developed in the late 1960s and early 1970s by Baum
Results 1 - 10
of
35,019
