Pages that link to "Item:Q4366231"
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The following pages link to Improvements on Cross-Validation: The .632+ Bootstrap Method (Q4366231):
Displaying 50 items.
- The benefit of data-based model complexity selection via prediction error curves in time-to-event data (Q63246) (← links)
- Classifier variability: accounting for training and testing (Q411930) (← links)
- A kernel PLS based classification method with missing data handling (Q513700) (← links)
- Estimation of prediction error by using \(K\)-fold cross-validation (Q692962) (← links)
- Selection bias in working with the top genes in supervised classification of tissue samples (Q713676) (← links)
- Evaluating incremental values from new predictors with net reclassification improvement in survival analysis (Q746472) (← links)
- Sample size determination for training cancer classifiers from microarray and RNA-seq data (Q746699) (← links)
- Evaluation of new service development strategies using multicriteria analysis: predicting the success of innovative hospitality services (Q839981) (← links)
- An experimental comparison of cross-validation techniques for estimating the area under the ROC curve (Q901569) (← links)
- Bootstrap-based model selection criteria for beta regressions (Q905106) (← links)
- Bundling classifiers by bagging trees (Q957281) (← links)
- Bootstrap estimated true and false positive rates and ROC curve (Q961178) (← links)
- Multiclass classification and gene selection with a stochastic algorithm (Q961825) (← links)
- Estimating classification error rate: repeated cross-validation, repeated hold-out and bootstrap (Q961845) (← links)
- An empirical study of PLAD regression using the bootstrap (Q964609) (← links)
- A survey of cross-validation procedures for model selection (Q975579) (← links)
- Embedding sample points uncertainty measures in learning algorithms (Q1003548) (← links)
- Exact bootstrap \(k\)-nearest neighbor learners (Q1009331) (← links)
- Ensemble component selection for improving ICA based microarray data prediction models (Q1015183) (← links)
- Double-bagging: Combining classifiers by bootstrap aggregation (Q1402702) (← links)
- An alternative objective function for fitting regression trees to functional response variables (Q1658306) (← links)
- A center sliding Bayesian binary classifier adopting orthogonal polynomials (Q1678700) (← links)
- Predicting human behavior in unrepeated, simultaneous-move games (Q1682705) (← links)
- Searching for the optimum value of the smoothing parameter for a radial basis function surface with feature area by using the bootstrap method (Q1699378) (← links)
- Bootstrapping the out-of-sample predictions for efficient and accurate cross-validation (Q1722724) (← links)
- Bolstered error estimation (Q1764066) (← links)
- Bandwidth choice for nonparametric classification (Q1781162) (← links)
- The MELBS team winning entry for the EVA2017 competition for spatiotemporal prediction of extreme rainfall using generalized extreme value quantiles (Q1792637) (← links)
- Assessing classifiers in terms of the partial area under the ROC curve (Q1800075) (← links)
- Least angle regression. (With discussion) (Q1879940) (← links)
- On the biases of error estimators in prediction problems (Q1903172) (← links)
- Resampling-based information criteria for best-subset regression (Q1925995) (← links)
- Model selection by resampling penalization (Q1951992) (← links)
- Applying randomness effectively based on random forests for classification task of datasets of insufficient information (Q1952805) (← links)
- Unsupervised stratification of cross-validation for accuracy estimation (Q1978232) (← links)
- Modeling of the algal atypical increase in La Barca reservoir using the DE optimized least square support vector machine approach with feature selection (Q1997725) (← links)
- Stochastic optimization with adaptive restart: a framework for integrated local and global learning (Q2022223) (← links)
- An extended two-stage sequential optimization approach: properties and performance (Q2023982) (← links)
- Consistent validation of gray-level thresholding image segmentation algorithms based on machine learning classifiers (Q2065280) (← links)
- Estimation of varying coefficient models with measurement error (Q2172010) (← links)
- Block-regularized repeated learning-testing for estimating generalization error (Q2201671) (← links)
- An improved methodology for filling missing values in spatiotemporal climate data set. Application to Tanganyika Lake data set (Q2267325) (← links)
- Optimality of training/test size and resampling effectiveness in cross-validation (Q2317259) (← links)
- On the predictive risk in misspecified quantile regression (Q2330755) (← links)
- A new variable selection approach using random forests (Q2361222) (← links)
- Forecast of the higher heating value in biomass torrefaction by means of machine learning techniques (Q2424937) (← links)
- Bayesian classification for bivariate normal gene expression (Q2445655) (← links)
- Measuring the prediction error. A comparison of cross-validation, bootstrap and covariance penalty methods (Q2445750) (← links)
- Cross-validated bagged learning (Q2474238) (← links)
- Two-group classification via a biobjective margin maximization model (Q2497257) (← links)
