discrete hidden Markov model
A transition-constrained discrete hidden Markov model for automatic sleep staging
... Method: The EEG, EOG and EMG signals from twenty subjects were measured. In addition to selecting sleep characteristics based on the 1968 R&K rules, features utilized in other research were collected. Thirteen ...
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Speech to Text Conversion Using Discrete Hidden Markov Model
... ABSTRACT:In recent years, Speech Recognition has the great development in the automation industry. This paper proposes an Automatic Speech Recognition (ASR) to facilitate an interaction between human and the electronic ...
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Application of Hidden Markov Model to locate soccer robots
... a discrete Hidden Markov Model is used for segmentation of the observed trajectories which requires the recorded continuous trajectories to be mapped into a codebook of discrete values ...
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A Hybrid Feature Extraction Approach for Human Action Recognition System based on Skeleton Data
... ABSTRACT: Human action recognition has potential to impact a wide range of applications from surveillance to human computer interfaces to content based video retrieval. In this paper, the human actions are classified in ...
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Investing in Copper Futures: Evaluation of Absolute Return Strategies Within a Discrete-State Hidden Markov Model
... using hidden Markov models under the assumption that the return series of the COMEX Copper futures are generated from a multivariate normal ...this model reasonably represent the market; the states ...
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A hidden Markov model for matching spatial networks
... First of all, we intuitively assign higher probabilities to close features. Haussdorf dis- tance [1] or modified median Hausdorff distance are generally used, as presented in [40]. This approach computes distances from ...
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Further applications of higher-order Markov chains and developments in regime-switching models
... the model identi- fication and outlines how their method could be applied to the asset allocation problem using mean-variance type utility ...[2], Markov Chain Monte Carlo methods were applied to estimate a ...
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Mixed Hidden Markov Models for Clinical Research with Discrete Repeated Measurements
... effect model with a Poisson regression to these data [23], which include a random intercept as well as a random ...the model assumes that the data have a tendency to increase or decrease over ...regression ...
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Hidden Markov model signal processing and control
... on-line model identification for finite-discrete state, discrete-time hidden Markov models (HMMs) with continuous-range ...the Markov model, so as to gain a better ...
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PERFORMANCE COMPARISION OF ENVIRONMENTAL NOISE MODELLING USING HIDDEN MARKOV MODEL AND FUZZY HIDDEN MARKOV MODEL
... HMM model only streams of observations are ...with discrete observations HMM is less effective as the model parameters are initialized with random values, but taking into consideration the ...any ...
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Neural Hidden Markov Model for Machine Translation
... based model that has a recurrent bidirectional en- coder and a recurrent decoder, but use no atten- tion ...are discrete ran- dom variables and (unlike attention levels) must be ...
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On Parsing Visual Sequences with the Hidden Markov Model
... a discrete 3-state left-to-right HMM model for each highlight type, noting that the three states correspond well to the evolution of the highlights in terms of characteristic ...
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Hidden Markov Model for Time Series Prediction
... The Hidden Markov Model (HMM) is a powerful statistical tool for modeling generative sequences that can be characterized by an underlying process generating an observable ...sequence. Hidden ...
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Offline Signature Verification Using the Discrete Radon Transform and a Hidden Markov Model
... a model and a ques- tioned ...the model and a questioned signature, the writer dependant informa- tion embedded at the substroke level is examined and un- ballistic motion and tremor information in each ...
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Space time rainfall modeling using hidden markov model
... The Hidden Markov model is a doubly stochastic process in which the rainfall observation distribution depends on several unobserved discrete states (Rabiner and Juang, ...The Hidden ...
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Quantifying the uncertainty in change points
... a model with independent errors (an AR(0) error process) and no detrending is ...and discrete cosine basis detrending (Ashburner et ...error model. An AR(1) model for fMRI time series is ...
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Personalized Marketing in Facebook using Hidden Markov Model
... mining model can be used to customize the content of the advertisement or to figure out the date and the time it should be placed ...statistical model HMM was used to predict all possible future states, so ...
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The recognition system of sickle cell anemia by using hidden markov model
... the Hidden Markov Models of recognition to the mutation that causes one of the most common genetic diseases, Sickle Cell disease, thus diagnoses the person state (infected, uninfected) , This method is ...
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Clustering with Hidden Markov Model on Variable Blocks
... We first identified the top 500 highly variable genes based on their sample variances after applying log transformation. We then fit this reduced data of size 300 × 500 using HMM-VB. The genes are divided into 5 variable ...
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A Comparative Study of Phoneme Recognition using GMM HMM and ANN based Acoustic Modeling
... The Hidden Markov Models assume a Gaussian Mixture model (with a variable number of clusters) in each of the states of the ...acoustic model in HMM based speech recognition system usually ...
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