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Regression Error Characteristic Surfaces
 In Proc. of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'05), ACM
, 2005
"... This paper presents a generalization of Regression Error Characteristic (REC) curves. REC curves describe the cumulative distribution function of the prediction error of models and can be seen as a generalization of ROC curves to regression problems. REC curves provide useful information for analyzi ..."
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Cited by 7 (1 self)
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This paper presents a generalization of Regression Error Characteristic (REC) curves. REC curves describe the cumulative distribution function of the prediction error of models and can be seen as a generalization of ROC curves to regression problems. REC curves provide useful information
Regression Error Characteristic Curves
"... Receiver Operating Characteristic (ROC) curves provide a powerful tool for visualiTr ing and comparing classification results. Regression Error Characteristic (REC) curves generalize ROC curves to regression. REC curves plot the error tolerance on the xaxis versus the percentage of points predicte ..."
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Receiver Operating Characteristic (ROC) curves provide a powerful tool for visualiTr ing and comparing classification results. Regression Error Characteristic (REC) curves generalize ROC curves to regression. REC curves plot the error tolerance on the xaxis versus the percentage of points
Regression Error Characteristic CurVes
 Proceedings of the 20th International Conference on Machine Learning
, 2003
"... Receiver Operating Characteristic (ROC) curves provide a powerful tool for visualizing and comparing classification results. Regression Error Characteristic (REC) curves generalize ROC curves to regression. REC curves plot the error tolerance on the xaxis versus the percentage of points predicted wi ..."
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Cited by 35 (0 self)
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Receiver Operating Characteristic (ROC) curves provide a powerful tool for visualizing and comparing classification results. Regression Error Characteristic (REC) curves generalize ROC curves to regression. REC curves plot the error tolerance on the xaxis versus the percentage of points predicted
Research Track Poster Regression Error Characteristic Surfaces
"... This paper presents a generalization of Regression Error Characteristic (REC) curves. REC curves describe the cumulative distribution function of the prediction error of models and can be seen as a generalization of ROC curves to regression problems. REC curves provide useful information for analyzi ..."
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This paper presents a generalization of Regression Error Characteristic (REC) curves. REC curves describe the cumulative distribution function of the prediction error of models and can be seen as a generalization of ROC curves to regression problems. REC curves provide useful information
Boosting for Regression Using Regression Error Characteristic Curves
 In Proceedings of the ICML 2005 Workshop on ROC Analysis in Machine Learning (ROCML
, 2005
"... Boosting is one of the most popular methods for constructing ensembles. The objective of this work is to present a boosting algorithm for regression based on the RegressorBoosting algorithm, in which we propose the use of REC curves in order to select a good threshold value, so that only residuals ..."
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Cited by 1 (1 self)
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Boosting is one of the most popular methods for constructing ensembles. The objective of this work is to present a boosting algorithm for regression based on the RegressorBoosting algorithm, in which we propose the use of REC curves in order to select a good threshold value, so that only residuals
Using Regression Error Characteristic Curves for Model Selection in Ensembles of Neural Networks
"... Abstract. Regression Error Characteristic (REC) analysis is a technique for evaluation and comparison of regression models that facilitates the visualization of the performance of many regression functions simultaneously in a single graph. The objective of this work is to present a new approach for ..."
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Abstract. Regression Error Characteristic (REC) analysis is a technique for evaluation and comparison of regression models that facilitates the visualization of the performance of many regression functions simultaneously in a single graph. The objective of this work is to present a new approach
Bootstrap Confidence Intervals for Regression Error Characteristic Curves Evaluating the Prediction Error of Software Cost Estimation Models
"... The importance of Software Cost Estimation at the early stages of the development life cycle is clearly portrayed by the utilization of several algorithmic and artificial intelligence models and methods, appeared so far in the literature. Despite the several comparison studies, there seems to be a d ..."
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technique, namely the bootstrap method in order to evaluate the standard error and bias of the accuracy measures, whereas bootstrap confidence intervals are constructed for the Regression Error Characteristic curves. The tool can be applied to any cost estimation situation in order to study the behavior
Least angle regression
, 2004
"... The purpose of model selection algorithms such as All Subsets, Forward Selection and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will be applied. Typically we have available a large collection of possible covariates from which we hope to s ..."
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Cited by 1326 (37 self)
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to select a parsimonious set for the efficient prediction of a response variable. Least Angle Regression (LARS), a new model selection algorithm, is a useful and less greedy version of traditional forward selection methods. Three main properties are derived: (1) A simple modification of the LARS algorithm
On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes
, 2001
"... We compare discriminative and generative learning as typified by logistic regression and naive Bayes. We show, contrary to a widely held belief that discriminative classifiers are almost always to be preferred, that there can often be two distinct regimes of performance as the training set size is i ..."
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Cited by 520 (8 self)
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We compare discriminative and generative learning as typified by logistic regression and naive Bayes. We show, contrary to a widely held belief that discriminative classifiers are almost always to be preferred, that there can often be two distinct regimes of performance as the training set size
ModelBased Analysis of Oligonucleotide Arrays: Model Validation, Design Issues and Standard Error Application
, 2001
"... Background: A modelbased analysis of oligonucleotide expression arrays we developed previously uses a probesensitivity index to capture the response characteristic of a specific probe pair and calculates modelbased expression indexes (MBEI). MBEI has standard error attached to it as a measure of ..."
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Cited by 775 (28 self)
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Background: A modelbased analysis of oligonucleotide expression arrays we developed previously uses a probesensitivity index to capture the response characteristic of a specific probe pair and calculates modelbased expression indexes (MBEI). MBEI has standard error attached to it as a measure
Results 1  10
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