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Algorithmic Polarization for Hidden Markov Models
[article]
2018
arXiv
pre-print
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Using a mild variant of polar codes we design linear compression schemes compressing Hidden Markov sources (where the source is a Markov chain, but whose state is not necessarily observable from its output ...
), and to decode from Hidden Markov channels (where the channel has a state and the error introduced depends on the state). ...
Further, the marginals of this product distribution are easily computed using dynamic programming (via what is called the "Forward Algorithm" for hidden Markov models, described for completeness in Appendix ...
arXiv:1810.01969v1
fatcat:vuzvclocv5he3kk7ztoklip4ym
Algorithmic Polarization for Hidden Markov Models
2018
Innovations in Theoretical Computer Science
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Using a mild variant of polar codes we design linear compression schemes compressing Hidden Markov sources (where the source is a Markov chain, but whose state is not necessarily observable from its output ...
), and to decode from Hidden Markov channels (where the channel has a state and the error introduced depends on the state). ...
Polarization for Hidden Markov Models 1. ...
doi:10.4230/lipics.itcs.2019.39
dblp:conf/innovations/GuruswamiNS19
fatcat:wb4q4pmwlfaotfuvdslwtmnody
Towards the Modeling of Behavioral Trajectories of Users in Online Social Media
[article]
2016
arXiv
pre-print
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First, we illustrate how to leverage the probabilistic framework provided by Hidden Markov Models (HMMs) to represent users by embedding the temporal sequences of actions they performed online. ...
In this paper, we introduce a methodology that allows to model behavioral trajectories of users in online social media. ...
Hidden Markov Models A Hidden Markov Model (HMM) is a probabilistic model in which the system being modelled is assumed to be a Markov process with unobserved (hidden) states. ...
arXiv:1611.05778v2
fatcat:e4nkr2vrqjhgzfu2v73tzcuzva
Markovian regularization of latent-variable-models mixture for New multi-component image reduction/segmentation scheme
2007
Signal, Image and Video Processing
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In this paper, we focus on the Probabilistic Principal Component Analysis (PPCA) as a latent variable model, and the Hidden Markov quad-Tree (HMT) as an a priori for regularization. ...
Linear models usually fail with complex data structure, and mixture of linear models, each of which modeling a local cluster of the data, is more suitable. ...
Hidden Markov quad-Tree In the past decade, Hidden Markov models have proved to be robust and efficient image analysis methods for many detection, denoising, segmentation, classification and pattern recognition ...
doi:10.1007/s11760-007-0010-y
fatcat:tj4r4ligvjbwval7kraqdkttde
CSI Learning Based Active Secure Coding Scheme For Detectable Wiretap Channel
[article]
2020
arXiv
pre-print
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First, we build a detectable wiretap channel model by combining the hidden Markov model with the compound wiretap channel model, in which the varying of channel block CSI is a Markov process and the detected ...
Next, we present a CSI learning scheme to learn the CSI from the detected information by the Baum-Welch and Viterbi algorithms. ...
The Viterbi Algorithm for CSI Decoding Then the Viterbi algorithm [21] for CSI decoding is as follow. Fig. 1 . 1 The Hidden Markov Model.
. 1, we have this stochastic mapping as (S, O, B). ...
arXiv:1807.02924v2
fatcat:pwur7tvorvainf7qvqxz7tnihi
Clustering of Mueller matrix images for skeletonized structure detection
2004
Optics Express
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Hidden Markov Chains Model (HMCM) and Hidden Hierarchical Markovian Model (HHMM) show to handle effectively Mueller images and give very good results for biological tissues (vegetal leaves). ...
This paper extends and refines previous work on clustering of polarization-encoded images. ...
Worms for his help. ...
doi:10.1364/opex.12.001271
pmid:19474947
fatcat:hwturm4w7rcmnmuin4a3kypkxi
Prediction of Signal Peptides in Proteins from Malaria Parasites
2018
International Journal of Molecular Sciences
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Therefore, we designed a new, more flexible and universal probabilistic model for recognition of atypical eukaryotic signal peptides. ...
The significance of signal peptides stimulates development of new computational methods for their detection. ...
Acknowledgments: We want to thank Paweł Błażej (University of Wrocław) for fruitful discussion about hidden semi-Markov models. ...
doi:10.3390/ijms19123709
pmid:30469512
fatcat:wwhlsezxz5fphgkj5q3xfo5kdy
Clustering of polarization-encoded images
2004
Applied Optics
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Two methods of analysis are proposed: polarization contrast enhancement and a more-sophisticated image-processing algorithm based on a Markovian model. ...
Polarization-encoded imaging consists of the distributed measurements of polarization parameters for each pixel of an image. We address clustering of multidimensional polarization-encoded images. ...
used for hidden Markov chains. ...
doi:10.1364/ao.43.000283
pmid:14735948
fatcat:r2uehhsnubeijbbm25srjnbzre
Sentiment Analysis on Urdu Tweets Using Markov Chains
2020
SN Computer Science
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This paper presents a sentiment analysis approach based on Markov chains for predicting the sentiment of Urdu tweets. ...
This work focuses on developing a 3-class (positive, negative, and neutral) sentiment classification model for the Urdu language. ...
[14] applied hidden Markov chains for morphological segmentation in the Mongolian language. Mongolian affixes were identified using hidden Markov models to perform segmentation. ...
doi:10.1007/s42979-020-00279-9
fatcat:zb6kn64v6zba7mt4svrnhgjkby
Markovian models for one dimensional structure estimation on heavily noisy imagery
[article]
2013
arXiv
pre-print
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In this work we propose to transform the edge strength detection problem into a binary segmentation problem in the undecimated wavelet domain, solvable using parallel 1d Hidden Markov Models. ...
For general dependency models, exact estimation of the state map becomes computationally complex, but in our model, exact MAP is feasible. ...
-Within each band, model each chain of pixel related coefficient as a 1d Hidden Markov Chain with hidden states "edge" and "no-edge". ...
arXiv:1304.7713v1
fatcat:ol4pipogindrno6nve4bzka3qy
Estimation of character diagram from open-movie databases for cultural understanding
2015
Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)
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By using Markov Logic Network, we can infer while allowing the violation of rules. ...
In this paper, we propose the estimation method of interpersonal relationships of characters from movie script databases on the Web using Markov Logic Network. ...
These rules determine the sentiment polarity for lines in the script using sentiment polarity for the word and favor between characters using the sentiment polarity for lines. ...
doi:10.1109/icosc.2015.7050808
dblp:conf/semco/OhwatariKSTO15
fatcat:hxismezhmjh4ragrms4gqxq4sa
Synthetic Text Generation for Sentiment Analysis
2015
Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
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In a series of experiments using different data sets and sentiment analysis methods, we show that generative models can generate texts with a specific sentiment and that hidden Markov model based text ...
In this paper, we present a preliminary study on different generative models for text generation, which maintain specific properties of natural language text, i.e., the sentiment of a review text. ...
Hidden Markov Model A hidden Markov model (HMM) is a Markov process with unobserved states and an observable variable (Rabiner, 1989) . ...
doi:10.18653/v1/w15-2922
dblp:conf/wassa/Maqsud15
fatcat:ykx7qkc7irct3pd2p4blfo3aoq
Deep Markov Neural Network for Sequential Data Classification
2015
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
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The general framework consists of two parts: a hidden Markov component and a recursive neural network component. ...
We present a general framework for incorporating sequential data and arbitrary features into language modeling. ...
This step can be done by using the Baum-Welch algorithm (Baum et al., 1970; Baum, 1972) for learning hidden Markov models. ...
doi:10.3115/v1/p15-2006
dblp:conf/acl/Yang15
fatcat:732z6psmubgdfey2igyupif2cq
Using Hidden Markov Model to Monitor Possible Loan Defaults in Banks
2020
International Journal of Economics and Business Administration
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Hidden Markov model was applied to the polarized data based on the sequence of the sentiment analysis. ...
Findings: Hidden Markov Model gives the transition probability of state, default or regular, for the observed polarized sentiments from Facebook data for borrowers. ...
Conclusion Hidden Markov Model is widely used for detecting the hidden state from the observations. ...
doi:10.35808/ijeba/653
fatcat:iq6gggmgzvbwldi42tmmhiptwm
Detection of Different Brain Diseases from EEG Signals Using Hidden Markov Model
2019
International Journal of Image Graphics and Signal Processing
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Index Terms-Electroencephalography (EEG), Hidden Markov Model (HMM), Baum-Welch algorithm (B-W algorithm), Initial probability matrix, Transition probability matrix. ...
EEG signals of several subjects which record electric potential caused by neurons firing in the brain are undergone a Hidden Markov Model (HMM) classification technique. ...
Hidden Markov Model (HMM) A hidden Markov model (HMM) is a state machine with two layers, namely state layer and observation layer (Fig.3 ). ...
doi:10.5815/ijigsp.2019.10.03
fatcat:oucxksly2vhtndnn27ff44qjfm
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