D > Machine Recognition,

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2Using change point detection to automate daily activity segmentation
S. Aminikhanghahi, и D. Cook. Proc. IEEE Int. Conf. Pervasive Computing and Communications Workshops (PerCom Workshops), стр. 262--267. (марта 2017)
4 лет назад , @annakrause
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
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2Named Entity Recognition from Greek texts: the GIE Project
V. Karkaletsis, C. Spyropoulos, и G. Petasis. том 21 из Intelligent Systems, Control and Automation: Science and Engineering, глава 12, стр. 131--142. Springer Berlin / Heidelberg, (1999)Presented at the 3rd European Robotics Intelligent Systems & Control Conference (EURISCON’98), June 22--25 1998, Athens, Greece..
13 лет назад , @petasis
extraction,
recognition,
machine
learning
named-entity
information
extraction,recognition,machinelearningnamed-entityinformation
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2SysBioMed report: Advancing systems biology for medical applications
O. Wolkenhauer, D. Fell, P. Meyts, N. Bluthgen, H. Herzel, N. Novere, T. Hofer, K. Schurrle, и I. van Leeuwen. IET Systems Biology, 3 (3): 131--136 (мая 2009)
14 лет назад , @ra1602
Briefing,
recognition,
dynamical
bioinformatics,
theory,
Rostock
gene,
clinical
cellular
learning,
subcellular
networks,
machine
diseases,
spatio-temporal
report
biology,
Policy
medical
statistical
molecular
modelling,
macroscale
information,
complex
Science
nonlinear
systems
pattern
SBI
property,
{SysBioMed}
encoding,
phenomena,
Briefing,recognition,dynamicalbioinformatics,theory,Rostockgene,clinicalcellularlearning,subcellularnetworks,machinediseases,spatio-temporalreportbiology,Policymedicalstatisticalmolecularmodelling,macroscaleinformation,complexSciencenonlinearsystemspatternSBIproperty,{SysBioMed}encoding,phenomena,
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2Object Class Recognition Using NEAT-Evolved Artificial Neural Network
M. Hasanat, S. Harun, D. Ramachandram, и M. Rajeswari. Computer Graphics, Imaging and Visualisation, 2008. CGIV '08. Fifth International Conference on, (августа 2008)
15 лет назад , @mozaher
robust
art
recognition,
histograms,
(artificial
artificial
learning,
network,
machine
Augmenting
support
object
ANN
Neuro
classifier,
Topologies,
statistical
SIFT
neural
nets,
learning
intelligence),
descriptor
recognitionNEAT-evolved
NEAT-evolved
vision,
class
computer
of
Evolution
feature,
vector
robustartrecognition,histograms,(artificialartificiallearning,network,machineAugmentingsupportobjectANNNeuroclassifier,Topologies,statisticalSIFTneuralnets,learningintelligence),descriptorrecognitionNEAT-evolvedNEAT-evolvedvision,classcomputerofEvolutionfeature,vector
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2Using change point detection to automate daily activity segmentation

2Named Entity Recognition from Greek texts: the GIE Project

2SysBioMed report: Advancing systems biology for medical applications

2Object Class Recognition Using NEAT-Evolved Artificial Neural Network

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публикации (спрятать)4 показатьвсётолько публикациипубликации на страницу5102050100 расширенный... RSSBibTeXRDFдальше...

2Using change point detection to automate daily activity segmentation

2Named Entity Recognition from Greek texts: the GIE Project

2SysBioMed report: Advancing systems biology for medical applications

2Object Class Recognition Using NEAT-Evolved Artificial Neural Network

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