ESWA
2007
14 years 1 days ago
2007
Hidden Markov Models, or HMMs for short, have been recently used in Bioinformatics for the classification of DNA or protein chains, giving rise to what is known as Profile Hidde...
JAIR
2006
14 years 2 days ago
2006
Logical hidden Markov models (LOHMMs) upgrade traditional hidden Markov models to deal with sequences of structured symbols in the form of logical atoms, rather than flat characte...
IJFCS
2008
14 years 3 days ago
2008
Hidden Markov models (HMMs) are effective tools to detect series of statistically homogeneous structures, but they are not well suited to analyse complex structures. Numerous meth...
BMCBI
2008
14 years 6 days ago
2008
Background: Tiling arrays are an important tool for the study of transcriptional activity, proteinDNA interactions and chromatin structure on a genome-wide scale at high resolutio...
ICPR
2010
IEEE
14 years 8 days ago
2010
IEEE
—We propose a statistical framework for high-level feature extraction that uses SIFT Gaussian mixture models (GMMs) and audio models. SIFT features were extracted from all the im...
ICASSP
2010
IEEE
14 years 8 days ago
2010
IEEE
Annotation of large multilingual corpora remains a challenge to the data-driven approach to speech research, especially for under-resourced languages. This paper presents crosslan...
ICML
2010
IEEE
14 years 1 months ago
2010
IEEE
Hidden Markov Models (HMMs) are important tools for modeling sequence data. However, they are restricted to discrete latent states, and are largely restricted to Gaussian and disc...
NIPS
1992
14 years 1 months ago
1992
This paper describes a technique for learning both the number of states and the topologyof Hidden Markov Models from examples. The inductionprocess starts with the most specific m...
NIPS
1994
14 years 1 months ago
1994
We introduce a recurrent architecture having a modular structure and we formulate a training procedure based on the EM algorithm. The resulting model has similarities to hidden Ma...
NAACL
1994
14 years 1 months ago
1994
This paper introduces a class of statistical mechanisms, called hidden understanding models, for natural language processing. Much of the framework for hidden understanding models...