tag :: Learning, recognition,

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публикации (спрятать)23
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1Convolution Neural Network Based Method for Biometric Recognition
A. |. CENTRAL ASIAN JOURNAL OF THEORETICAL AND APPLIED SCIENCE, 4 (8): 58-68 (августа 2023)
9 месяцев назад , @centralasian_20
Ear
convolutional
extraction,
features
learning,
machine
network
networksneural
neural
recognition,
Earconvolutionalextraction,featureslearning,machinenetworknetworksneuralneuralrecognition,
(0)
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3Can deep learning beat numerical weather prediction?
M. Schultz, C. Betancourt, B. Gong, F. Kleinert, M. Langguth, L. Leufen, A. Mozaffari, и S. Stadtler. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379 (2194): 20200097 (февраля 2021)Publisher: Royal Society.
год назад , @jascal_panetzky
AI
deep
ecomodelling
learning,
machine
numerical
pattern
prediction,
recognition,
spatiotemporal
weather
AIdeepecomodellinglearning,machinenumericalpatternprediction,recognition,spatiotemporalweather
(0)
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2Machine Learning for Precipitation Nowcasting from Radar Images
S. Agrawal, L. Barrington, C. Bromberg, J. Burge, C. Gazen, и J. Hickey. (декабря 2019)arXiv:1912.12132 cs, stat.
год назад , @jascal_panetzky
-
Computer
Learning
Learning,
Machine
Pattern
Recognition,
Science
Statistics
Vision
and
ecomodelling
-ComputerLearningLearning,MachinePatternRecognition,ScienceStatisticsVisionandecomodelling
(0)
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2Knowledge Representations in Technical Systems -- A Taxonomy
K. Scharei, F. Heidecker, и M. Bieshaar. arXiv e-prints, (2020)
3 лет назад , @ies
-
Artificial
Computer
Intelligence,
Learning,
Machine
Pattern
Recognition,
Robotics
Science
Vision
and
-ArtificialComputerIntelligence,Learning,MachinePatternRecognition,RoboticsScienceVisionand
(0)
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1Self-Adaptation of Activity Recognition Systems to New Sensors
D. Bannach, M. Jänicke, V. Rey, S. Tomforde, B. Sick, и P. Lukowicz. arXiv e-prints, (2017)
3 лет назад , @ies
-
68T05,
Computer
K.3.2
Learning,
Machine
Pattern
Recognition,
Science
Statistics
Vision
and
-68T05,ComputerK.3.2Learning,MachinePatternRecognition,ScienceStatisticsVisionand
(0)
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1Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning
C. Haase-Schütz, R. Stal, H. Hertlein, и B. Sick. arXiv e-prints, (2020)
3 лет назад , @ies
-
Computer
Learning
Learning,
Machine
Pattern
Recognition,
Science
Statistics
Vision
and
-ComputerLearningLearning,MachinePatternRecognition,ScienceStatisticsVisionand
(0)
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1A Smart Receptionist Implementing Facial Recognition and Voice Interaction
O. Ahmed, A. Hadaegh, и Y. Li. International Journal of Image Processing (IJIP), 15 (3): 37-47 (августа 2021)
3 лет назад , @cscjournals
Deep
Django
Face
Framework,
Image
Learning,
Processing
Recognition,
DeepDjangoFaceFramework,ImageLearning,ProcessingRecognition,
(0)
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1CONTRAST OF RESNET AND DENSENET BASED ON THE RECOGNITION OF SIMPLE FRUIT DATA SET
D. Tianye. International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE), 6 (1): 1-8 (января 2019)
5 лет назад , @ashvath
Computer
Convolutional
Deep
Image
Networks.
Neural
Object
learning,
processing,
recognition,
vision,
ComputerConvolutionalDeepImageNetworks.NeuralObjectlearning,processing,recognition,vision,
(0)
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1Design and Development of a 2D-Convolution CNN model for Recognition of Handwritten Gurumukhi and Devanagari Characters
I. Chhabra, и S. Narang. International Journal of Image Processing (IJIP), 12 (3): 72-82 (октября 2018)
6 лет назад , @cscjournals
Character
Convolution
Deep
Handwritten
Learning,
Network
Network,
Neural
Recognition,
CharacterConvolutionDeepHandwrittenLearning,NetworkNetwork,NeuralRecognition,
(0)
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2Object recognition using a neural network and invariant Zernike features
A. Khotanzad, и J. Lu. Computer Vision and Pattern Recognition, 1989. Proceedings CVPR '89., IEEE Computer Society Conference on, стр. 200--205. IEEE Computer Society, (1989)
14 лет назад , @cocus
Zernike
binary
classifiers,
concept,
features,
images,
invariancy
invariant
learning
learning,
minimum-mean-distance
multilayer
neighbourhood
nets,
network,
neural
pattern
perceptron,
recognition,
recognitionbackpropagation
rotation-invariant
scale
systems,
Zernikebinaryclassifiers,concept,features,images,invariancyinvariantlearninglearning,minimum-mean-distancemultilayerneighbourhoodnets,network,neuralpatternperceptron,recognition,recognitionbackpropagationrotation-invariantscalesystems,
(0)
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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,
Policy
Rostock
SBI
Science
bioinformatics,
biology,
cellular
clinical
complex
diseases,
dynamical
encoding,
gene,
information,
learning,
machine
macroscale
medical
modelling,
molecular
networks,
nonlinear
pattern
phenomena,
property,
recognition,
report
spatio-temporal
statistical
subcellular
systems
theory,
{SysBioMed}
Briefing,PolicyRostockSBISciencebioinformatics,biology,cellularclinicalcomplexdiseases,dynamicalencoding,gene,information,learning,machinemacroscalemedicalmodelling,molecularnetworks,nonlinearpatternphenomena,property,recognition,reportspatio-temporalstatisticalsubcellularsystemstheory,{SysBioMed}
(0)
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3Semantic Hierarchies for Visual Object Recognition
M. Marszalek, и C. Schmid. Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on, (июня 2007)
15 лет назад , @mozaher
accuracy,
appearance
detection,
hierarchies,
image
inference
labels
learning,
lexical
mechanisms,
networks,
networksclassification
object
recognition
recognition,
semantic
semantics,
tools,
visual
accuracy,appearancedetection,hierarchies,imageinferencelabelslearning,lexicalmechanisms,networks,networksclassificationobjectrecognitionrecognition,semanticsemantics,tools,visual
(0)
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14Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, и P. Haffner. Proceedings of the IEEE, 86 (11): 2278--2324 (1998)
15 лет назад , @mozaher
2D
GTN,
back-propagation,
backpropagation,
based
character
cheque
complex
convolution,
convolutional
decision
digit
document
extraction,
field
gradient
gradient-based
graph
handwritten
high-dimensional
language
learning
learning,
measure
minimization,
modeling,
multilayer
multimodule
network
networks,
neural
optical
patterns,
perceptrons,
performance
reading,
recognition
recognition,
recognizers,
segmentation
shape
surface
synthesis,
systems,
task,
technique,
transformer
variability,
2DGTN,back-propagation,backpropagation,basedcharacterchequecomplexconvolution,convolutionaldecisiondigitdocumentextraction,fieldgradientgradient-basedgraphhandwrittenhigh-dimensionallanguagelearninglearning,measureminimization,modeling,multilayermultimodulenetworknetworks,neuralopticalpatterns,perceptrons,performancereading,recognitionrecognition,recognizers,segmentationshapesurfacesynthesis,systems,task,technique,transformervariability,
(0)
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1On neurobiological, neuro-fuzzy, machine learning, and statistical pattern recognition techniques
A. Joshi, N. Ramakrishman, E. Houstis, и J. Rice. Neural Networks, IEEE Transactions on, 8 (1): 18--31 (1997)
15 лет назад , @mozaher
(artificial
adaptation,
algorithm,
analysis,
classes,
classification,
clustering,
fuzzy
human
intelligence),
learning
learning,
machine
method,
min-max
minimax
multiresolution
mutually
nets,
neural
neuro-fuzzy
neurobiological
nonexclusive
one-pass
online
pattern
recognition
recognition,
set
statistical
techniques,
theory,
(artificialadaptation,algorithm,analysis,classes,classification,clustering,fuzzyhumanintelligence),learninglearning,machinemethod,min-maxminimaxmultiresolutionmutuallynets,neuralneuro-fuzzyneurobiologicalnonexclusiveone-passonlinepatternrecognitionrecognition,setstatisticaltechniques,theory,
(0)
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2Learning and vision machines
B. Heisele, A. Verri, и T. Poggio. Proceedings of the IEEE, 90 (7): 1164--1177 (2002)
15 лет назад , @mozaher
(artificial
categorization,
classification,
computer
detection,
intelligence),
intelligence,
learning
learning,
machines,
models,
morphable
object
pattern
perception
perception,
recognition,
statistical
supervised
support
vector
vision,
visual
(artificialcategorization,classification,computerdetection,intelligence),intelligence,learninglearning,machines,models,morphableobjectpatternperceptionperception,recognition,statisticalsupervisedsupportvectorvision,visual
(0)
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4Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories
L. Fei-Fei, R. Fergus, и P. Perona. Computer Vision and Image Understanding, 106 (1): 59--70 (апреля 2007)
15 лет назад , @mozaher
Bayesian
Categorization,
Generative
Incremental
Object
learning,
model
model,
recognition,
BayesianCategorization,GenerativeIncrementalObjectlearning,modelmodel,recognition,
(0)
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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
(artificial
ANN
Augmenting
Evolution
NEAT-evolved
Neuro
SIFT
Topologies,
art
artificial
class
classifier,
computer
descriptor
feature,
histograms,
intelligence),
learning
learning,
machine
nets,
network,
neural
object
of
recognition,
recognitionNEAT-evolved
robust
statistical
support
vector
vision,
(artificialANNAugmentingEvolutionNEAT-evolvedNeuroSIFTTopologies,artartificialclassclassifier,computerdescriptorfeature,histograms,intelligence),learninglearning,machinenets,network,neuralobjectofrecognition,recognitionNEAT-evolvedrobuststatisticalsupportvectorvision,
(0)
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2Improving Object Detection Performance with Genetic Programming
M. Zhang. International Journal on Artificial Intelligence Tools, 16 (5): 849--873 (октября 2007)
17 лет назад , @brazovayeye
algorithms,
fitness
function,
genetic
learning,
networks
neural
object
program
programming,
recognition,
size,
target
two-phase
algorithms,fitnessfunction,geneticlearning,networksneuralobjectprogramprogramming,recognition,size,targettwo-phase
(0)
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2A Domain-independent Window approach to Multiclass Object Detection using Genetic Programming
M. Zhang, V. Ciesielski, и P. Andreae. EURASIP Journal on Applied Signal Processing, 2003 (8): 841--859 (июля 2003)Special Issue on Genetic and Evolutionary Computation for Signal Processing and Image Analysis.
17 лет назад , @brazovayeye
algorithms,
computer
detection,
genetic
learning,
machine
networks,
neural
object
programming,
recognition,
target
vision
algorithms,computerdetection,geneticlearning,machinenetworks,neuralobjectprogramming,recognition,targetvision
(0)
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1Genetic Programming, Proceedings of EuroGP'2001
J. Miller, M. Tomassini, P. Lanzi, C. Ryan, A. Tettamanzi, и W. Langdon (Ред.) том 2038 из LNCS, Lake Como, Italy, Springer-Verlag, (18-20 April 2001)
17 лет назад , @brazovayeye
Active
Animat,
Arm,
Artificial
BDD,
Bloat,
Block-oriented
Boolean
CAD,
Causality,
Cellular
Character
Complexity,
Computational
Contour
Controller
Crossover
DNA,
Developmental
Discovery,
Dynamic
Evolution
Evolution,
Evolvability,
Evolvable
Extraction,
Feature
Fitness,
Fixed
Function
Generator,
Genetic
Genotype-Phenotype
Grammatical
Hardware,
Image
Intrinsic
Inverse
Iterated
Kinematics,
Knowledge
Layered
Learning,
Length
Linear
MAX
Machine
Mapping,
Multi-expression
Multipopulation
Neutral
One-then-zeros
Parallel
Pattern
Polymorphism,
Problem
Problem,
Process
Program
Reasoning,
Recognition,
Regression,
Retina,
Robotic
STGP,
Series
Symbolic
Systems,
Time
Trees,
Turing
VHDL,
VLSI
algorithms,
bias,
control,
design,
detection,
distributed
distributions,
function
genetic
individuals,
landscape,
machines,
model,
modelling,
modular
mutation,
of
points,
prediction,
problem,
processing,
programming,
representation,
representations,
robot,
robust,
size,
structures,
ActiveAnimat,Arm,ArtificialBDD,Bloat,Block-orientedBooleanCAD,Causality,CellularCharacterComplexity,ComputationalContourControllerCrossoverDNA,DevelopmentalDiscovery,DynamicEvolutionEvolution,Evolvability,EvolvableExtraction,FeatureFitness,FixedFunctionGenerator,GeneticGenotype-PhenotypeGrammaticalHardware,ImageIntrinsicInverseIteratedKinematics,KnowledgeLayeredLearning,LengthLinearMAXMachineMapping,Multi-expressionMultipopulationNeutralOne-then-zerosParallelPatternPolymorphism,ProblemProblem,ProcessProgramReasoning,Recognition,Regression,Retina,RoboticSTGP,SeriesSymbolicSystems,TimeTrees,TuringVHDL,VLSIalgorithms,bias,control,design,detection,distributeddistributions,functiongeneticindividuals,landscape,machines,model,modelling,modularmutation,ofpoints,prediction,problem,processing,programming,representation,representations,robot,robust,size,structures,
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1Convolution Neural Network Based Method for Biometric Recognition

3Can deep learning beat numerical weather prediction?

2Machine Learning for Precipitation Nowcasting from Radar Images

2Knowledge Representations in Technical Systems -- A Taxonomy

1Self-Adaptation of Activity Recognition Systems to New Sensors

1Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning

1A Smart Receptionist Implementing Facial Recognition and Voice Interaction

1CONTRAST OF RESNET AND DENSENET BASED ON THE RECOGNITION OF SIMPLE FRUIT DATA SET

1Design and Development of a 2D-Convolution CNN model for Recognition of Handwritten Gurumukhi and Devanagari Characters

2Object recognition using a neural network and invariant Zernike features

2SysBioMed report: Advancing systems biology for medical applications

3Semantic Hierarchies for Visual Object Recognition

14Gradient-based learning applied to document recognition

1On neurobiological, neuro-fuzzy, machine learning, and statistical pattern recognition techniques

2Learning and vision machines

4Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories

2Object Class Recognition Using NEAT-Evolved Artificial Neural Network

2Improving Object Detection Performance with Genetic Programming

2A Domain-independent Window approach to Multiclass Object Detection using Genetic Programming

1Genetic Programming, Proceedings of EuroGP'2001

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