tag :: learning, algorithm,

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1Harnessing AI for Global Marketing Practices An Integrated Secondary Review of Scholarly Works
P. Sarkar. INTERNATIONAL JOURNAL OF TREND IN SCIENTIFIC RESEARCH AND DEVELOPMENT, 8 (1): 842-850 (February 2024)
8 months ago by @ijtsrd
show all tags
Artificial
Machine
Marketing,
algorithm,
customer-centric
deep
intelligence,
learning
learning,
ArtificialMachineMarketing,algorithm,customer-centricdeepintelligence,learninglearning,
(0)
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1Algorithm of Minds AoM Algorithms That Control You
S. Ashwin. INTERNATIONAL JOURNAL OF TREND IN SCIENTIFIC RESEARCH AND DEVELOPMENT, 8 (1): 810-315 (February 2024)
8 months ago by @ijtsrd
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Algorithm
Algorithm,
Algorithms
Algorithms,
Collaborative
Impact
Learning,
Machine
Manipulation,
Ranking
filtering,
minds,
of
AlgorithmAlgorithm,AlgorithmsAlgorithms,CollaborativeImpactLearning,MachineManipulation,Rankingfiltering,minds,of
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1ELECTRONIC TOURISM AS A DESTINATION MARKETING STRATEGY FOR WORLD TRAVELLERS
R. R. CENTRAL ASIAN JOURNAL OF MATHEMATICAL THEORY AND COMPUTER SCIENCES, 4 (5): 9-28 (May 2023)
a year ago by @centralasian_20
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Algorithm,
Cost
Estimation
Extraction,
Forecasting,
Learning,
Machine
POI
Recommendation
Seasonal
System,
Weather
Algorithm,CostEstimationExtraction,Forecasting,Learning,MachinePOIRecommendationSeasonalSystem,Weather
(0)
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1Fast Gradient-Descent Methods for Temporal-Difference Learning with Linear Function Approximation
R. Sutton, H. Maei, D. Precup, S. Bhatnagar, D. Silver, {. Szepesvári, and E. Wiewiora. ICML, page 993--1000. (2009)
5 years ago by @csaba
show all tags
GTD2,
TDC
algorithm,
approximation,
function
gradient
learning,
online
prediction,
reinforcement
stochastic
theory,
GTD2,TDCalgorithm,approximation,functiongradientlearning,onlineprediction,reinforcementstochastictheory,
(0)
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1Learning When to Stop Thinking and Do Something!
B. Póczos, Y. Abbasi-Yadkori, {. Szepesvári, R. Greiner, and N. Sturtevant. ICML, page 825--832. (2009)
5 years ago by @csaba
show all tags
Cross-Entropy
REINFORCE,
algorithm,
application,
gradient
learning,
pondering,
reinforcement
search
Cross-EntropyREINFORCE,algorithm,application,gradientlearning,pondering,reinforcementsearch
(0)
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1Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation
H. Maei, {. Szepesvári, S. Bhatnagar, D. Silver, D. Precup, and R. Sutton. NIPS, page 1204--1212. (2009)
5 years ago by @csaba
show all tags
GTD
algorithm,
approximation,
function
gradient
learning,
networks,
neural
nonlinear
online
prediction,
reinforcement
stochastic
theory,
GTDalgorithm,approximation,functiongradientlearning,networks,neuralnonlinearonlineprediction,reinforcementstochastictheory,
(0)
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1A Convergent O(n) Algorithm for Off-policy Temporal-difference Learning with Linear Function Approximation
R. Sutton, {. Szepesvári, and H. Maei. NIPS, page 1609--1616. (2008)
5 years ago by @csaba
show all tags
GTD
algorithm,
approximation,
function
gradient
learning,
online
prediction,
reinforcement
stochastic
theory,
GTDalgorithm,approximation,functiongradientlearning,onlineprediction,reinforcementstochastictheory,
(0)
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1Toward Off-Policy Learning Control with Function Approximation
H. Maei, {. Szepesvári, S. Bhatnagar, and R. Sutton. ICML, page 719--726. Omnipress, (June 2010)
5 years ago by @csaba
show all tags
algorithm,
approximation,
control
function
gradient
learning,
networks
neural
nonlinear
online
reinforcement
stochastic
theory,
algorithm,approximation,controlfunctiongradientlearning,networksneuralnonlinearonlinereinforcementstochastictheory,
(0)
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1A method to design a neural pattern recognition system by using a genetic algorithm with partial fitness and a deterministic mutation
M. Fukumi, and N. Akamatsu. Systems, Man, and Cybernetics, 1996. IEEE International Conference on, 3, page 1989--1993. IEEE Computer Society, (1996)
14 years ago by @cocus
show all tags
(artificial
Fourier
algorithm,
algorithms,
coin
convergence,
deterministic
features
fitness,
genetic
intelligence),
invariant
learning
learning,
multilayer
multilayered
mutation,
network
network,
neural
partial
pattern
perceptrons,
problem,
recognition
recognitionFourier
rotated
rotation
spectra,
system,
transform,
transforms,
(artificialFourieralgorithm,algorithms,coinconvergence,deterministicfeaturesfitness,geneticintelligence),invariantlearninglearning,multilayermultilayeredmutation,networknetwork,neuralpartialpatternperceptrons,problem,recognitionrecognitionFourierrotatedrotationspectra,system,transform,transforms,
(0)
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2Designing a neural network by a genetic algorithm with partial fitness
M. Fukumi, S. Omatu, and Y. Nishikawa. Proceedings of the IEEE International joint Conference on Neural Networks 1995, IJCNN'95, 4, page 1834--1838. IEEE Computer Society, (November 1995)
14 years ago by @cocus
show all tags
(artificial
Fourier
algorithm,
algorithms,
chromosome,
coin
convergence
convergence,
crossover
feedforward
fitness,
genetic
intelligence),
learning
learning,
methods,
nets,
network,
neural
numerical
object
of
operation
operation,
partial
recognition
recognitionFourier
selection
spectra,
system,
transform
transform,
(artificialFourieralgorithm,algorithms,chromosome,coinconvergenceconvergence,crossoverfeedforwardfitness,geneticintelligence),learninglearning,methods,nets,network,neuralnumericalobjectofoperationoperation,partialrecognitionrecognitionFourierselectionspectra,system,transformtransform,
(0)
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2Applying two-level simulated annealing on Bayesian structure learning to infer genetic networks
T. Wang, J. Touchman, and G. Xue. page 647-648. (August 2004)
15 years ago by @mozaher
show all tags
(artificial
Bayesian
Carlo
K2
Monte
algorithm,
annealing
belief
biology
computing,
genetic
genetics,
global
inference
inference,
intelligence),
learning
learning,
local
mechanisms,
method,
methods,
model,
network
networks,
optimized
optimizer,
probability
probability,
problem,
problems,
search
simulated
strengthened
structure
two-level
(artificialBayesianCarloK2Montealgorithm,annealingbeliefbiologycomputing,geneticgenetics,globalinferenceinference,intelligence),learninglearning,localmechanisms,method,methods,model,networknetworks,optimizedoptimizer,probabilityprobability,problem,problems,searchsimulatedstrengthenedstructuretwo-level
(0)
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1On neurobiological, neuro-fuzzy, machine learning, and statistical pattern recognition techniques
A. Joshi, N. Ramakrishman, E. Houstis, and J. Rice. Neural Networks, IEEE Transactions on, 8 (1): 18--31 (1997)
15 years ago by @mozaher
show all tags
(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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22Genetic Algorithms in Search, Optimization and Machine Learning
D. Goldberg. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, (1989)
16 years ago by @selmarsmit
show all tags
algorithm,
genetic,
learning,
optimization
algorithm,genetic,learning,optimization
(1)
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2Blind separation of convolutive mixtures of cyclostationary sources using an extended natural gradient method
W. Wang, M. Jafari, S. Sanei, and J. Chambers. Signal Processing and Its Applications, 2003. Proceedings. Seventh International Symposium on, (July 2003)
16 years ago by @foadq
show all tags
(artificial
adaptive
algorithm,
blind
convergence
convolutive
cyclostationary
gradient
intelligence),
iterative
learning
learning,
method
methods,
mixtures,
natural
numerical
of
separation
separation,
signals,
source
speed,
statistics
(artificialadaptivealgorithm,blindconvergenceconvolutivecyclostationarygradientintelligence),iterativelearninglearning,methodmethods,mixtures,naturalnumericalofseparationseparation,signals,sourcespeed,statistics
(0)
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1Evolutionary Principles in Self-Referential Learning. On Learning now to Learn: The Meta-Meta-Meta...-Hook
J. Schmidhuber. Technische Universitat Munchen, Germany, Diploma Thesis, (14 May 1987)
17 years ago by @brazovayeye
show all tags
EURISKO,
PSALM,
SALM,
algorithm,
algorithms,
associative
brigade,
bucket
evolution,
fractals
genetic
genetical
introsepection,
learning,
meta,
nets,
neuronal
programming
self-reference,
EURISKO,PSALM,SALM,algorithm,algorithms,associativebrigade,bucketevolution,fractalsgeneticgeneticalintrosepection,learning,meta,nets,neuronalprogrammingself-reference,
(0)
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1Flexible neural trees ensemble for stock index modeling
Y. Chen, B. Yang, and A. Abraham. Neurocomputing, 70 (4-6): 697--703 (January 2007)Advanced Neurocomputing Theory and Methodology - Selected papers from the International Conference on Intelligent Computing 2005 (ICIC 2005), International Conference on Intelligent Computing 2005.
17 years ago by @brazovayeye
show all tags
Ensemble
Flexible
GP-like
Particle
Stock
algorithm,
algorithms,
evolutionary
genetic
index
learning,
neural
optimisation,
programming,
structure-based
swarm
tree
tree,
EnsembleFlexibleGP-likeParticleStockalgorithm,algorithms,evolutionarygeneticindexlearning,neuraloptimisation,programming,structure-basedswarmtreetree,
(0)
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1GECCO 2003: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference
A. Barry (Eds.) Chigaco, AAAI, (11 July 2003)
17 years ago by @brazovayeye
show all tags
agents,
algorithm,
algorithms,
annealing,
case-based
classifier
co-evolution,
computation,
design
evolution
evolution,
evolutionary
fuzzy
genetic
grammatical
implementation,
learning
learning,
machine
model,
multi-objective
optimization,
parallel
planning,
prediction,
programming,
reasoning,
representations
scheduling,
series
simulated
strategies,
system,
time
agents,algorithm,algorithms,annealing,case-basedclassifierco-evolution,computation,designevolutionevolution,evolutionaryfuzzygeneticgrammaticalimplementation,learninglearning,machinemodel,multi-objectiveoptimization,parallelplanning,prediction,programming,reasoning,representationsscheduling,seriessimulatedstrategies,system,time
(0)
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1GECCO 2002: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference
A. Barry (Eds.) New York, AAAI, (8 July 2002)
17 years ago by @brazovayeye
show all tags
agents,
algorithm,
algorithms,
annealing,
applications,
case-based
classifier
co-evolution,
computation,
design
evolution
evolution,
evolutionary
fuzzy
genetic
grammatical
implementation,
industrial
learning
learning,
linkage
machine
model,
multi-objective
niching,
optimization,
parallel
planning,
prediction,
programming,
reasoning,
scheduling,
series
simulated
strategies,
system,
time
agents,algorithm,algorithms,annealing,applications,case-basedclassifierco-evolution,computation,designevolutionevolution,evolutionaryfuzzygeneticgrammaticalimplementation,industriallearninglearning,linkagemachinemodel,multi-objectiveniching,optimization,parallelplanning,prediction,programming,reasoning,scheduling,seriessimulatedstrategies,system,time
(0)
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1Some Formal Analysis of Rocchio's Similarity-Based Relevance Feedback Algorithm
Z. Chen, and B. Zhu. Inf. Retr., 5 (1): 61--86 (January 2002)
17 years ago by @pprett
show all tags
algorithm,
feedback,
learning,
machine,
relevance,
rocchio,
supervised,
theoretical
algorithm,feedback,learning,machine,relevance,rocchio,supervised,theoretical
(0)
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2New methods for relevance feedback: improving information retrieval performance
P. Biron, and D. Kraft. SAC '95: Proceedings of the 1995 ACM symposium on Applied computing, page 482--487. New York, NY, USA, ACM, (1995)
17 years ago by @pprett
show all tags
algorithm,
connectionist,
feedback,
genetic,
learning,
relevance
algorithm,connectionist,feedback,genetic,learning,relevance
(0)
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1Harnessing AI for Global Marketing Practices An Integrated Secondary Review of Scholarly Works

1Algorithm of Minds AoM Algorithms That Control You

1ELECTRONIC TOURISM AS A DESTINATION MARKETING STRATEGY FOR WORLD TRAVELLERS

1Fast Gradient-Descent Methods for Temporal-Difference Learning with Linear Function Approximation

1Learning When to Stop Thinking and Do Something!

1Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation

1A Convergent O(n) Algorithm for Off-policy Temporal-difference Learning with Linear Function Approximation

1Toward Off-Policy Learning Control with Function Approximation

1A method to design a neural pattern recognition system by using a genetic algorithm with partial fitness and a deterministic mutation

2Designing a neural network by a genetic algorithm with partial fitness

2Applying two-level simulated annealing on Bayesian structure learning to infer genetic networks

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

22Genetic Algorithms in Search, Optimization and Machine Learning

2Blind separation of convolutive mixtures of cyclostationary sources using an extended natural gradient method

1Evolutionary Principles in Self-Referential Learning. On Learning now to Learn: The Meta-Meta-Meta...-Hook

1Flexible neural trees ensemble for stock index modeling

1GECCO 2003: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference

1GECCO 2002: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference

1Some Formal Analysis of Rocchio's Similarity-Based Relevance Feedback Algorithm

2New methods for relevance feedback: improving information retrieval performance

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