J. > markov | BibSonomy

publications (hide)85
display
all
publications only
publications per page
5
10
20
50
100
sort by
added at
title
author
publication date
entry type
help for advanced sorting...
RSS
BibTeX
RDF
more...

2An Evaluation Study of Recognizing Conducting Gesture Using Computational Intelligence Techniques
J. v. Heek, J. Park, X. Yu, and H. Tsang. 2018 IEEE Symposium Series on Computational Intelligence (SSCI), page 1005-1012. (November 2018)
3 years ago by @herberttsang
show all tags
recognition;Gyroscopes;Computational
Markov
in
tsang_pub
Analysis;Multimedia
Processing;Learning
models;Conductors;Gesture
and
intelligence;Mobile
Vision
Hidden
Signal
handsets;Accelerometers;Feature
Pattern
Recognition
recognition;Gyroscopes;ComputationalMarkovintsang_pubAnalysis;MultimediaProcessing;Learningmodels;Conductors;Gestureandintelligence;MobileVisionHiddenSignalhandsets;Accelerometers;FeaturePatternRecognition
copydeleteadd this publication to your clipboard
4Large vocabulary continuous speech recognition using HTK
P. Woodland, J. Odell, V. Valtchev, and S. Young. Proceedings of the 1994 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2, page 125-128. Adelaide, Australia, (April 1994)
4 years ago by @m-toman
show all tags
triphones;decision
toolkit;single
pass
tied-state
recognition;cross-word
tree
density
speech
systems;trigram
models;natural
data;Vocabulary
Wall
language
hidden
independent
Journal
network
Group;HTK;bigram
analysis;Hidden
models;word-internal
theory;decoding;hidden
state
triphones;portable
models;mixture
languages;software
based
Markov
decision
modelling;Buildings;Decision
testing;Training
recognition;vocabulary;ARPA
models;continuous
trees;Decoding;Error
University
Speech
rate;gender
software
gender
recognition
task;Cambridge
tools;speech
models;Software
tools;Speech
clustering;error
decoder;speech
Street
recognition;System
dynamic
dependent
triphones;decisiontoolkit;singlepasstied-staterecognition;cross-wordtreedensityspeechsystems;trigrammodels;naturaldata;VocabularyWalllanguagehiddenindependentJournalnetworkGroup;HTK;bigramanalysis;Hiddenmodels;word-internaltheory;decoding;hiddenstatetriphones;portablemodels;mixturelanguages;softwarebasedMarkovdecisionmodelling;Buildings;Decisiontesting;Trainingrecognition;vocabulary;ARPAmodels;continuoustrees;Decoding;ErrorUniversitySpeechrate;gendersoftwaregenderrecognitiontask;Cambridgetools;speechmodels;Softwaretools;Speechclustering;errordecoder;speechStreetrecognition;Systemdynamicdependent
copydeleteadd this publication to your clipboard
2Using change point detection to automate daily activity segmentation
S. Aminikhanghahi, and D. Cook. Proc. IEEE Int. Conf. Pervasive Computing and Communications Workshops (PerCom Workshops), page 262--267. (March 2017)
4 years ago by @annakrause
show all tags
image
models,
segmentation,
Segmentation,
Markov
detection
recognition,
(artificial
automatic
real
Smart
methods,
home
transition
homes,
Change
Context,
daily
machine
change
algorithm,
datasets,
time
boundaries,
object
Real-time
activity
Activity
recognition
learning
learning-based
intelligence),
automation,
Feature
systems,
Recognition,
smart
point
algorithms,
aware
extraction,
continuous
feature
detection,
Hidden
segmentation
streaming,
human
imagemodels,segmentation,Segmentation,Markovdetectionrecognition,(artificialautomaticrealSmartmethods,hometransitionhomes,ChangeContext,dailymachinechangealgorithm,datasets,timeboundaries,objectReal-timeactivityActivityrecognitionlearninglearning-basedintelligence),automation,Featuresystems,Recognition,smartpointalgorithms,awareextraction,continuousfeaturedetection,Hiddensegmentationstreaming,human
copydeleteadd this publication to your clipboard
1Compressed Conditional Mean Embeddings for Model-Based Reinforcement Learning
G. Lever, J. Shawe-Taylor, R. Stafford, and {. Szepesvári. AAAI-2016, page 1779--1787. (November 2016)
5 years ago by @csaba
show all tags
approximation,
Markov
pseudo-MDPs
model-based
abstraction,
Decision
control
planning,
Processes,function
RL,
learning,
reinforcement
control,
approximation,Markovpseudo-MDPsmodel-basedabstraction,Decisioncontrolplanning,Processes,functionRL,learning,reinforcementcontrol,
copydeleteadd this publication to your clipboard
1Universal Option Models
H. Yao, {. Szepesvári, R. Sutton, J. Modayil, and S. Bhatnagar. NIPS, page 990--998. (September 2014)
5 years ago by @csaba
show all tags
approximation,
Markov
learning,
LSTD
difference
Decision
reinforcement
control
control,
temporal
planning,
Processes,function
approximation,Markovlearning,LSTDdifferenceDecisionreinforcementcontrolcontrol,temporalplanning,Processes,function
copydeleteadd this publication to your clipboard
1Compression-Based Parts-of-Speech Tagger for The Arabic Language
I. Alkhazi, and W. Teahan. International Journal of Computational Linguistics (IJCL), 10 (1): 1 - 15 (April 2019)
5 years ago by @cscjournals
show all tags
Model,
Markov
Arabic
Model
Statistical
Hidden
Language
Tagger,
Natural
Processing,
Part-of-Speech
Model,MarkovArabicModelStatisticalHiddenLanguageTagger,NaturalProcessing,Part-of-Speech
copydeleteadd this publication to your clipboard
1Diffusive Nested Sampling
B. Brewer, L. Pártay, and G. Csányi. Statistics and Computing, 21 (4): 649--656 (2011)
6 years ago by @rwhender
show all tags
Markov
chain
Bayesian
Computation;
Sampling;
Nested
Carlo
Monte
MarkovchainBayesianComputation;Sampling;NestedCarloMonte
copydeleteadd this publication to your clipboard
4Bayesian nonparametric hidden semi-Markov models
M. Johnson, and A. Willsky. Journal of Machine Learning Research, 14 (Feb): 673--701 (2013)
7 years ago by @becker
show all tags
bayesian
time4all
citedby:scholar:count:182
hidden
nonparametric
non
bayes
series
unsupervised
parametric
semi
time
model
markov
citedby:scholar:timestamp:2018-3-13
bayesiantime4allcitedby:scholar:count:182hiddennonparametricnonbayesseriesunsupervisedparametricsemitimemodelmarkovcitedby:scholar:timestamp:2018-3-13
copydeleteadd this publication to your clipboard
1Markov random field segmentation of brain MR images
K. Held, E. Kops, B. Krause, W. Wells, R. Kikinis, and H. Muller-Gartner. Transactions on Medical Imaging, 16 (6): 878--886 (1997)
8 years ago by @becker
show all tags
random
chain
inthesis
field
diss
citedby:scholar:timestamp:2017-4-7
network
citedby:scholar:count:530
markov
randomchaininthesisfielddisscitedby:scholar:timestamp:2017-4-7networkcitedby:scholar:count:530markov
copydeleteadd this publication to your clipboard
1Continuous-time Markov chains: An applications-oriented approach
W. Anderson. Springer, New York, NY, (2012)
8 years ago by @becker
show all tags
chain
inthesis
citedby:scholar:count:914
continuous
time
diss
markov
citedby:scholar:timestamp:2017-4-6
chaininthesiscitedby:scholar:count:914continuoustimedissmarkovcitedby:scholar:timestamp:2017-4-6
copydeleteadd this publication to your clipboard
1Approaching the limit of predictability in human mobility
X. Lu, E. Wetter, N. Bharti, A. Tatem, and L. Bengtsson. Scientific Reports, (October 2013)
8 years ago by @becker
show all tags
geo
mobility
inthesis
limit
prediction
diss
spatial
markov
geomobilityinthesislimitpredictiondissspatialmarkov
copydeleteadd this publication to your clipboard
1Trip Destination Prediction Based on Past GPS Log Using a Hidden Markov Model
J. Alvarez-Garcia, J. Ortega, L. Gonzalez-Abril, and F. Velasco. Expert Syst. Appl., 37 (12): 8166--8171 (December 2010)
8 years ago by @becker
show all tags
geo
predicition
trip
inthesis
hidden
gps
destination
model
spatial
dess
markov
geopredicitiontripinthesishiddengpsdestinationmodelspatialdessmarkov
copydeleteadd this publication to your clipboard
2Variable Length Markov Chains
P. Buhlmann, and A. Wyner. Annals of Statistics, (1999)
8 years ago by @becker
show all tags
chain
inthesis
length
diss
model
markov
variable
chaininthesislengthdissmodelmarkovvariable
copydeleteadd this publication to your clipboard
4Bayesian Nonparametric Hidden semi-Markov Models
M. Johnson, and A. Willsky. Journal of Machine Learning Research, 14 (1): 673--701 (February 2013)
8 years ago by @wipster
show all tags
toread
bayes
hmm
machine
learning
model
markov
toreadbayeshmmmachinelearningmodelmarkov
copydeleteadd this publication to your clipboard
4Bayesian Nonparametric Hidden semi-Markov Models
M. Johnson, and A. Willsky. Journal of Machine Learning Research, 14 (1): 673--701 (February 2013)
8 years ago by @jaeschke
show all tags
bayes
hmm
machine
learning
model
markov
bayeshmmmachinelearningmodelmarkov
copydeleteadd this publication to your clipboard
3Taste Over Time: The Temporal Dynamics of User Preferences
J. Moore, S. Chen, D. Turnbull, and T. Joachims. Conference of the International Society for Music Information Retrieval Conference (ISMIR), page 401-406. (2013)
8 years ago by @becker
show all tags
sequence
music
inthesis
embedding
diss
analysis
logistic
markov
sequencemusicinthesisembeddingdissanalysislogisticmarkov
copydeleteadd this publication to your clipboard
1Componentwise adaptation for high dimensional MCMC
H. Haario, E. Saksman, and J. Tamminen. Computational Statistics, 20 (2): 265--273 (2005)
8 years ago by @yourwelcome
show all tags
Markov
Metropolis-within-GIBBS
chain
Carlo,
adaptive,
Monte
MarkovMetropolis-within-GIBBSchainCarlo,adaptive,Monte
copydeleteadd this publication to your clipboard
1Markov modulated Poisson processes for clustered line transect data
H. Skaug. Environmental and Ecological Statistics, 13 (2): 199--211 (June 2006)
8 years ago by @yourwelcome
show all tags
Markov
NOAA
processes,
cetacean,
sea,
Poisson,
distribution
modelling
species
overdispersion,
\_tablet,
clustering,
abundance,
key,
MarkovNOAAprocesses,cetacean,sea,Poisson,distributionmodellingspeciesoverdispersion,\_tablet,clustering,abundance,key,
copydeleteadd this publication to your clipboard
1Ensemble filter techniques for intermittent data assimilation - a survey
C. Cotter, and S. Reich. arXiv:1208.6572, (August 2012)
8 years ago by @yourwelcome
show all tags
Markov
chain
Carlo,
assimilation,
adaptive,
data
simulations
review,
Monte
MarkovchainCarlo,assimilation,adaptive,datasimulationsreview,Monte
copydeleteadd this publication to your clipboard
1Hierarchical Bayes for structured, variable populations: from recapture data to life-history prediction
J. Clark, G. Ferraz, N. Oguge, H. Hays, and J. DiCostanzo. Ecology, 86 (8): 2232--2244 (2005)
8 years ago by @yourwelcome
show all tags
Markov
chain
processes,
Bayesian
birds,
distribution,
monte
model,
multivariate
Monte
inference,
random
effects
Dirichlet-multinomial
Carlo,
prior
carlo
hierarchical
mammals,
chain,
capture-recapture,
markov
Markovchainprocesses,Bayesianbirds,distribution,montemodel,multivariateMonteinference,randomeffectsDirichlet-multinomialCarlo,priorcarlohierarchicalmammals,chain,capture-recapture,markov
copydeleteadd this publication to your clipboard

⟨⟨
⟨
1
2
3
⟩
⟩⟩

publications (hide)85 displayallpublications onlypublications per page5102050100 sort byadded attitleauthorpublication dateentry typehelp for advanced sorting... RSSBibTeXRDFmore...

related tags

tags

publications (hide)85
display
all
publications only
publications per page
5
10
20
50
100
sort by
added at
title
author
publication date
entry type
help for advanced sorting...
RSS
BibTeX
RDF
more...