Search Results - (( using estimation using algorithm ) OR ( variable selection based algorithm ))
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1
Variable block based motion estimation using hexagon diamond full search algorithm (HDFSA) via block subtraction technique
Published 2015“…The threshold calculation is based on the video selected. Each video selected will have its own threshold which is used for the block size selection. …”
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Robust diagnostics and variable selection procedure based on modified reweighted fast consistent and high breakdown estimator for high dimensional data
Published 2022“…Sure screening-based correlation methods are popular tools used to select the most significant variables in the true model in sparse and high dimensional analysis. …”
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3
Model selection approaches of water quality index data
Published 2016“…Automatic model selection by using algorithm can avoid huge variability in model specification process compared to manual selection.With the employment of algorithm, the right model selected is then also used for forecasting purposes. …”
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Extended multiple models selection algorithms based on iterative feasible generalized least squares (IFGLS) and expectation-maximization (EM) algorithm
Published 2019“…However, SURE-Autometrics has not been estimated using maximum likelihood estimation (MLE). …”
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Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition
Published 2007“…In the selection phase, a new variable length genetic algorithm is applied to select the lexical cues. …”
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Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli
Published 2018“…In the developed feature selection approach, multi-objective binary-valued backtracking search algorithm (MOBBSA) is used as an efficient evolutionary search algorithm to search within different combinations of input variables and selects the non-dominated feature subsets, which minimize simultaneously both the estimation error and the number of features. …”
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An adaptive routing algorithm: enhanced confidence-based Q routing algorithm in network traffic
Published 2004“…Confidence-based Q (CQ) Routing Algorithm is an adaptive network routing algorithm. …”
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Enhanced Adaptive Confidence-Based Q Routing Algorithms For Network Traffic
Published 2004“…These two adaptive routing algorithms enhance the existing Confidence-based Q (CQ) and Confidence-based Dual Reinforcement Q (CDRQ) Routing Algorithms. …”
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9
Enhancing Model Selection Based On Penalized Regression Methods And Empirical Mode Decomposition
Published 2021“…These components have been used in several studies as new predictor variables to predict the behaviour of the response variable. …”
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10
Penalized LAD-SCAD estimator based on robust wrapped correlation screening method for high dimensional models
Published 2021“…The proposed estimator is denoted as WCS+LAD-SCAD and will be employed for variable selection. …”
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11
Robust multivariate least angle regression
Published 2017“…The least angle regression selection (LARS) algorithms that use the classical sample means, variances, and correlations between the original variables are very sensitive to the presence of outliers and other contamination. …”
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Short-term electricity price forecasting in deregulated electricity market based on enhanced artificial intelligence techniques / Alireza Pourdaryaei
Published 2020“…It is used to search within a number of input variables combinations and to select the feature subsets, which minimizes simultaneously vice-versa the estimation error and the feature numbers. …”
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14
Information Theoretic-based Feature Selection for Machine Learning
Published 2018“…Also, additional experiments to compare the relative performance of the IFS with five related feature selection algorithms were carried out using natural domain datasets. …”
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15
Determination of tree stem volume : A case study of Cinnamomum
Published 2013“…A modeling approach is developed which focuses on the phases in the model-building procedures, effects of interactions variables on the model, minimizing the effects of multicollinearity on the variables and recommending remedial techniques to overcome them, identification of the significant variables by removing insignificant variables, selecting the best model using the eight selection criteria (8SCs), and finally using the residual analysis to validate the chosen best model. …”
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16
Modelling the yield loss of oil palm due to Ganoderma Basal Stem Rot disease
Published 2016“…For estimation-post-selection approach, there were two subset selection algorithms were applied, namely backward stepwise subset selection and best-subset selection. …”
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Modelling the yield loss of oil palm due to ganoderma basal stem rot disease
Published 2016“…For estimation-post-selection approach, there were two subset selection algorithms were applied, namely backward stepwise subset selection and best-subset selection. …”
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18
Robust correlation feature selection based support vector machine approach for high dimensional datasets
Published 2025“…Correlation-based feature selection methods are popular tools used to select the most important variables to include the true model in the analysis of sparse and high-dimensional models. …”
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Assessment of predictive models for chlorophyll-a concentration of a tropical lake.
Published 2011“…HEA model used parameters selected using genetic algorithm (GA). …”
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Assessment of predictive models for chlorophyll-a concentration of a tropical lake.
Published 2011“…HEA model used parameters selected using genetic algorithm (GA). …”
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