[PDF] Top 20 A theory of cross-validation error
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A theory of cross-validation error
... use cross-validation testing, but they did report the error on the training ...lower cross-validation error than instance-based ...lower cross-validation ... See full document
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Theoretical analyses of cross-validation error and voting in instance-based learning
... general theory of cross-validation error for algorithms that predict real-valued ...The theory is then applied to linear regression and instance-based ...The theory of ... See full document
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Stratifying tumour subtypes based on copy number alteration profiles using next-generation sequence data
... Classification error from 100 cross validations (52 and 50 observations in the training set and validation set, respectively) in the oral cancer dataset using only the smooth- and CBS-segmented CNA ... See full document
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Improved Decision Tree Induction Algorithm with Feature Selection, Cross Validation, Model Complexity and Reduced Error Pruning
... The important task of classification process is to classify new and unseen sample correctly. C5.0 is a classifier which gives efficient classification in less time compare to other classifier. Memory usage is less in ... See full document
5
Noise Tolerant Variants of the Perceptron Algorithm
... using cross validation on a separate validation ...different validation set ...to error bounds in the batch setting even in the unrealizable ... See full document
22
Estimating misclassification error: a closer look at cross validation based methods
... (bootstrap cross-validation or BCV), which they compared to LOOCV and to the ...squared error (MSE) represented by the mean squared bias (MSB) over N Monte Carlo simulations (discussed in Methods ... See full document
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b) Noise in the classes : It is given by the errors introduced
... In other methods, the instances considered potentially as noise are detected and removed using C4.5 (John 1995, Zhu et al., 2003), or neural networks (Zeng and Martinez, 2003). Some authors (John, 1995, Brodley and ... See full document
6
ANN Based Modeling for Prediction of Evaporation in Reservoirs (RESEARCH NOTE)
... and validation to assess the potential linear regression based modeling and ANN based modeling in predicting the evaporation loss from the ...squared error (RMSE) by using cross-validation to ... See full document
8
Anglicized Words and Misspelled Cognates in Native Language Identification
... Table 3: 10-fold cross-validation accuracy for cognates, L2-ed words, their combination, and when combined with spelling error SE character n-grams on the TOEFL4 and ICLE4 datasets, and [r] ... See full document
10
Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods
... 2011; Teutschbein and Seibert, 2012). The success of the nonparametric transformations is likely related to their flexi- bility as they do not rely on any predetermined function. This flexibility allows good fits to any ... See full document
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Incorporating landscape characteristics in a distance metric for interpolating between observations of stream water chemistry
... Abstract. Spatial patterns of water chemistry along stream networks can be quantified using synoptic or “snapshot” sampling. The basic idea is to sample stream water at many points over a relatively short period of time. ... See full document
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Sentiment Analysis of Twitter Data using Naïve Bayes with Unigram Approach
... K-fold cross validation acts as an improvement of the hideout ...k-fold cross validation ...K-fold cross validation method is that it takes k times more computational time than ... See full document
5
Estimating the Confidence Interval for Prediction Errors of Support Vector Machine Classifiers
... prediction error of SVM clas- sifiers based on cross-validated point estimators and their corresponding interval ...sample theory for the cross-validated estimators, and present a ... See full document
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Evaluation of Analysis by Cross-Validation. Part II: Diagnostic and Optimization of Analysis Error Covariance
... uncorrelated with the background error. Right panel, with correlated errors. the analysis error variance) is minimum. Yet, the principles and theory still hold in that case[r] ... See full document
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Fast approximate STEM image simulations from a machine learning model
... individual cross validation folds, 2-fold cross validation with two repetitions, 378-term ...RMS error as a function of number of terms in the regres- sion model for Pt and Pt-Mo ... See full document
10
Cokriging for evaluating agricultural pollution
... Based on the assumption that the cokriging process had produced acceptable error values for the entire data set, cross-validation involved similarity of variance tests in which each of t[r] ... See full document
6
Variogram Modeling Of Lime Saturation Factor On Limestone Quarry
... the cross-validation technique for its calculation. Cross-validation furthermore is used to graphically compare the original value of data to the estimated value, while repeating the search ... See full document
6
Analysis of Variance of Cross-Validation Estimators of the Generalization Error
... of cross-validation (CV) estimators of the generalization ...generalization error as a problem in approximating the moments of a ...generalization error is a function of the moments of the ... See full document
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PAPER WITHDRAWN
... or error theory include; (1) Wear and tear theory, it sounds perfectly reasonable to many people (2) Cross-linkage and cellular garbage theories, an accumulation of cross-linked ... See full document
10
Confidence intervals for the estimated smoothing parameter in nonparametric regression
... Confidence intervals are constructed for the smoothing parameter that minimizes average squared error using the asymptotic distribution of the cross-validation function and by a version [r] ... See full document
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