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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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for analyzing the performance of models, particularly when compared to error statistics like for instance the Mean Squared Error. In this paper we present Regression Error Characteristic (REC) surfaces that introduce a further degree of detail by plotting the cumulative distribution function of the errors
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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for analyzing the performance of models, particularly when compared to error statistics like for instance the Mean Squared Error. In this paper we present Regression Error Characteristic (REC) surfaces that introduce a further degree of detail by plotting the cumulative distribution function of the errors
Nonparametric regression with errors in variables
 Annals of Statistics
, 1993
"... The effect of errors in variables in nonparametric regression estimation is examined. To account for errors in covariates, deconvolution is involved in the construction ofa new class of kernel estimators. It is shown that optima/local and global rates of convergence of these kernel estimators can be ..."
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Cited by 84 (1 self)
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The effect of errors in variables in nonparametric regression estimation is examined. To account for errors in covariates, deconvolution is involved in the construction ofa new class of kernel estimators. It is shown that optima/local and global rates of convergence of these kernel estimators can
Evaluating the predictive performance of habitat models developed using logistic regression
 Ecological Modelling
, 2000
"... The use of statistical models to predict the likely occurrence or distribution of species is becoming an increasingly important tool in conservation planning and wildlife management. Evaluating the predictive performance of models using independent data is a vital step in model development. Such eva ..."
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Cited by 191 (3 self)
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). Lack of reliability can be attributed to two systematic sources, calibration bias and spread. Techniques are described for evaluating both of these sources of error. The discrimination capacity of logistic regression models is often measured by crossclassifying observations and predictions in a two
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
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
Improving the Accuracy and Speed of Support Vector Machines
 Advances in Neural Information Processing Systems 9
, 1997
"... Support Vector Learning Machines (SVM) are finding application in pattern recognition, regression estimation, and operator inversion for illposed problems. Against this very general backdrop, any methods for improving the generalization performance, or for improving the speed in test phase, of SVMs ..."
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Cited by 192 (23 self)
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in the error rate on 10,000 NIST test digit images of 1.4% to 1.0%. The method for improving the speed (the "reduced set" method) does so by approximating the support vector decision surface. We apply this method to achieve a factor of fifty speedup in test phase over the virtual support vector
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
Surface versus edgebased determinants of visual recognition
 Cogn Psychol
, 1988
"... Two roles hypothesized for surface characteristics, such as color, brightness, and texture, in object recognition are that such information can (a) define the gradients needed for a 2%D sketch so that a 3D representation can be derived (e.g., Marr & Nishihara, 1978) and (b) provide additional ..."
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Cited by 129 (1 self)
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Two roles hypothesized for surface characteristics, such as color, brightness, and texture, in object recognition are that such information can (a) define the gradients needed for a 2%D sketch so that a 3D representation can be derived (e.g., Marr & Nishihara, 1978) and (b) provide additional
Efficiently combining positions and normals for precise 3d geometry
 ACM Transactions on Graphics (Proc. SIGGRAPH
, 2005
"... not use color information in order to focus on geometric aspects. Note how our method eliminates noise from the range image while introducing real detail. The surface normals are of the same quality or better than those from photometric stereo, while most of the lowfrequency bias has been eliminate ..."
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Cited by 131 (9 self)
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triangulation and photometric stereo, which have different errorvs.frequency characteristics. We demonstrate the ability of our technique to both recover highfrequency details and avoid lowfrequency bias, producing surfaces that are more widely applicable than position or orientation data alone. 1
Results 1  10
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