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The use of the area under the ROC curve in the evaluation of machine learning algorithms
 PATTERN RECOGNITION
, 1997
"... In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multilayer Perceptron, kNe ..."
Abstract

Cited by 686 (3 self)
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sensitivity in Analysis of Variance (ANOVA) tests; a standard error that decreased as both AUC and the number of test samples increased; decision threshold independent; and it is invariant to a priori class probabilities. The paper concludes with the recommendation that AUC be used in preference to overall
A scaled conjugate gradient algorithm for fast supervised learning
 NEURAL NETWORKS
, 1993
"... A supervised learning algorithm (Scaled Conjugate Gradient, SCG) with superlinear convergence rate is introduced. The algorithm is based upon a class of optimization techniques well known in numerical analysis as the Conjugate Gradient Methods. SCG uses second order information from the neural netwo ..."
Abstract

Cited by 452 (0 self)
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network but requires only O(N) memory usage, where N is the number of weights in the network. The performance of SCG is benchmarked against the performance of the standard backpropagation algorithm (BP) [13], the conjugate gradient backpropagation (CGB) [6] and the onestep Broyden
Unsupervised learning of finite mixture models
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2002
"... This paper proposes an unsupervised algorithm for learning a finite mixture model from multivariate data. The adjective ªunsupervisedº is justified by two properties of the algorithm: 1) it is capable of selecting the number of components and 2) unlike the standard expectationmaximization (EM) alg ..."
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Cited by 419 (22 self)
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This paper proposes an unsupervised algorithm for learning a finite mixture model from multivariate data. The adjective ªunsupervisedº is justified by two properties of the algorithm: 1) it is capable of selecting the number of components and 2) unlike the standard expectationmaximization (EM
Combined Object Categorization and Segmentation With An Implicit Shape Model
 In ECCV workshop on statistical learning in computer vision
, 2004
"... We present a method for object categorization in realworld scenes. Following a common consensus in the field, we do not assume that a figureground segmentation is available prior to recognition. However, in contrast to most standard approaches for object class recognition, our approach automatical ..."
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Cited by 405 (10 self)
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novel MDLbased criterion. In addition, we present an extensive evaluation of our method on a standard dataset for car detection and compare its performance to existing methods from the literature. Our results show that the proposed method significantly outperforms previously published methods while
AUC optimization vs. error rate minimization
 Advances in Neural Information Processing Systems
, 2004
"... The area under an ROC curve (AUC) is a criterion used in many applications to measure the quality of a classification algorithm. However, the objective function optimized in most of these algorithms is the error rate and not the AUC value. We give a detailed statistical analysis of the relationship ..."
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Cited by 148 (2 self)
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The area under an ROC curve (AUC) is a criterion used in many applications to measure the quality of a classification algorithm. However, the objective function optimized in most of these algorithms is the error rate and not the AUC value. We give a detailed statistical analysis of the relationship
The earth is round (p < .05
 American Psychologist
, 1994
"... After 4 decades of severe criticism, the ritual of null hypothesis significance testing—mechanical dichotomous decisions around a sacred.05 criterion—still persists. This article reviews the problems with this practice, including its nearuniversal misinterpretation ofp as the probability that Ho is ..."
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Cited by 370 (0 self)
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After 4 decades of severe criticism, the ritual of null hypothesis significance testing—mechanical dichotomous decisions around a sacred.05 criterion—still persists. This article reviews the problems with this practice, including its nearuniversal misinterpretation ofp as the probability that Ho
On the coherence of AUC
"... Abstract. The area under the ROC curve (AUC) is a wellknown measure of ranking performance, and is also often used as a measure of classification performance, aggregating over decision thresholds as well as class and cost skews. However, David Hand has recently argued that AUC is fundamentally inco ..."
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Cited by 1 (1 self)
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Abstract. The area under the ROC curve (AUC) is a wellknown measure of ranking performance, and is also often used as a measure of classification performance, aggregating over decision thresholds as well as class and cost skews. However, David Hand has recently argued that AUC is fundamentally
An EM Algorithm for WaveletBased Image Restoration
, 2002
"... This paper introduces an expectationmaximization (EM) algorithm for image restoration (deconvolution) based on a penalized likelihood formulated in the wavelet domain. Regularization is achieved by promoting a reconstruction with lowcomplexity, expressed in terms of the wavelet coecients, taking a ..."
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Cited by 352 (22 self)
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process requiring O(N log N) operations per iteration. Thus, it is the rst image restoration algorithm that optimizes a waveletbased penalized likelihood criterion and has computational complexity comparable to that of standard wavelet denoising or frequency domain deconvolution methods. The convergence
Efficient AUC Maximization with Regularized
"... Area under the receiver operating characteristics curve (AUC) is a popular measure for evaluating the quality of binary classifiers, and intuitively, machine learning algorithms that maximize an approximation of AUC should have a good AUC performance when classifying new examples. However, designing ..."
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an approximation of AUC and has a closedform solution. Second, we show that this AUCRLS algorithm is computationally as efficient as the standard RLS algorithm that maximizes an approximation of the accuracy. Third, we compare the performance of these two algorithms in the task of assigning topic labels
Aucs/tr0202
"... This paper introduces a standalone case/knowledge acquisition tool, called COCKATOO (ConstraintCapable Knowledge Acquisition Tool), which uses an Extended BNF grammar to represent the main characteristics of the (domain) cases. Further, we also took the opportunity to build a tool that is both ..."
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This paper introduces a standalone case/knowledge acquisition tool, called COCKATOO (ConstraintCapable Knowledge Acquisition Tool), which uses an Extended BNF grammar to represent the main characteristics of the (domain) cases. Further, we also took the opportunity to build a tool that is both more flexible and powerful by augmenting the contextfree grammars with the expressiveness of constraints. COCKATOO was implemented using the SCREAMER+ declarative constraints package. Additionally, the paper discusses several uses of the tool by both the developers and, more significantly, by a group of knowledge engineers.
Results 1  10
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