Results 1  10
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187
Assessment and Propagation of Model Uncertainty
, 1995
"... this paper I discuss a Bayesian approach to solving this problem that has long been available in principle but is only now becoming routinely feasible, by virtue of recent computational advances, and examine its implementation in examples that involve forecasting the price of oil and estimating the ..."
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Cited by 108 (0 self)
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this paper I discuss a Bayesian approach to solving this problem that has long been available in principle but is only now becoming routinely feasible, by virtue of recent computational advances, and examine its implementation in examples that involve forecasting the price of oil and estimating the chance of catastrophic failure of the U.S. Space Shuttle.
The Willingness to Pay/Willingness to Accept Gap, the “Endowment Effect” and Experimental Procedures for Eliciting Valuations
, 2002
"... ..."
Context and hierarchy in a probabilistic image model
 in CVPR
, 2006
"... It is widely conjectured that the excellent ROC performance of biological vision systems is due in large part to the exploitation of context at each of many levels in a part/whole hierarchy. We propose a mathematical framework (a “composition machine”) for constructing probabilistic hierarchical ima ..."
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Cited by 64 (0 self)
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It is widely conjectured that the excellent ROC performance of biological vision systems is due in large part to the exploitation of context at each of many levels in a part/whole hierarchy. We propose a mathematical framework (a “composition machine”) for constructing probabilistic hierarchical image models, designed to accommodate arbitrary contextual relationships, and we build a demonstration system for reading Massachusetts license plates in an image set collected at Logan Airport. The demonstration system detects and correctly reads more than 98 % of the plates, with a negligible rate of false detection. Unlike a formal grammar, the architecture of a composition machine does not exclude the sharing of subparts among multiple entities, and does not limit interpretations to single trees (e.g. a scene can have multiple license plates, or no plates at all). In this sense, the architecture is more like a general Bayesian network than a formal grammar. On the other hand, unlike a Bayesian network, the distribution is nonMarkovian, and therefore more like a probabilistic contextsensitive grammar. The conceptualization and construction of a composition machine is facilitated by its formulation as the result of a series of nonMarkovian perturbations of a “Markov backbone. ” 1 1.
Generalization bounds for the area under the ROC curve
 Journal of Machine Learning Research
"... We study generalization properties of the area under an ROC curve (AUC), a quantity that has been advocated as an evaluation criterion for bipartite ranking problems. The AUC is a different and more complex term than the error rate used for evaluation in classification problems; consequently, existi ..."
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Cited by 48 (6 self)
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We study generalization properties of the area under an ROC curve (AUC), a quantity that has been advocated as an evaluation criterion for bipartite ranking problems. The AUC is a different and more complex term than the error rate used for evaluation in classification problems; consequently, existing generalization bounds for the classification error rate cannot be used to draw conclusions about the AUC. In this paper, we define a precise notion of the expected accuracy of a ranking function (analogous to the expected error rate of a classification function), and derive distributionfree probabilistic bounds on the deviation of the empirical AUC of a ranking function (observed on a finite data sequence) from its expected accuracy. We derive both a large deviation bound, which serves to bound the expected accuracy of a ranking function in terms of its empirical AUC on a test sequence, and a uniform convergence bound, which serves to bound the expected accuracy of a learned ranking function in terms of its empirical AUC on a training sequence. Our uniform convergence bound is expressed in terms of a new set of combinatorial parameters that we term the bipartite rankshatter coefficients; these play the same role in our result as do the standard shatter coefficients (also known variously as the counting numbers or growth function) in uniform convergence results for the classification error rate. We also compare our result with a recent uniform convergence result derived by Freund et al. (2003) for a quantity closely related to the AUC; as we show, the bound provided by our result is considerably tighter. 1 1
Efficient semiparametric estimation of quantile treatment effects
, 2003
"... This paper presents calculations of semiparametric efficiency bounds for quantile treatment effects parameters when selection to treatment is based on observable characteristics. The paper also presents three estimation procedures for these parameters, all of which have two steps: a nonparametric e ..."
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Cited by 46 (5 self)
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This paper presents calculations of semiparametric efficiency bounds for quantile treatment effects parameters when selection to treatment is based on observable characteristics. The paper also presents three estimation procedures for these parameters, all of which have two steps: a nonparametric estimation and a computation of the difference between the solutions of two distinct minimization problems. RootN consistency, asymptotic normality, and the achievement of the semiparametric efficiency bound is shown for one of the three estimators. In the final part of the paper, an empirical application to a job training program reveals the importance of heterogeneous treatment effects, showing that for this program the effects are concentrated in the upper quantiles of the earnings distribution.
Label Ranking by Learning Pairwise Preferences
"... Preference learning is an emerging topic that appears in different guises in the recent literature. This work focuses on a particular learning scenario called label ranking, where the problem is to learn a mapping from instances to rankings over a finite number of labels. Our approach for learning s ..."
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Cited by 46 (16 self)
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Preference learning is an emerging topic that appears in different guises in the recent literature. This work focuses on a particular learning scenario called label ranking, where the problem is to learn a mapping from instances to rankings over a finite number of labels. Our approach for learning such a mapping, called ranking by pairwise comparison (RPC), first induces a binary preference relation from suitable training data using a natural extension of pairwise classification. A ranking is then derived from the preference relation thus obtained by means of a ranking procedure, whereby different ranking methods can be used for minimizing different loss functions. In particular, we show that a simple (weighted) voting strategy minimizes risk with respect to the wellknown Spearman rank correlation. We compare RPC to existing label ranking methods, which are based on scoring individual labels instead of comparing pairs of labels. Both empirically and theoretically, it is shown that RPC is superior in terms of computational efficiency, and at least competitive in terms of accuracy.
Probability Density Estimation from Optimally Condensed Data Samples
 IEEE Trans. Pattern Analysis and Machine Intelligence
, 2003
"... Abstract—The requirement to reduce the computational cost of evaluating a point probability density estimate when employing a Parzen window estimator is a wellknown problem. This paper presents the Reduced Set Density Estimator that provides a kernelbased density estimator which employs a small per ..."
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Cited by 36 (0 self)
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Abstract—The requirement to reduce the computational cost of evaluating a point probability density estimate when employing a Parzen window estimator is a wellknown problem. This paper presents the Reduced Set Density Estimator that provides a kernelbased density estimator which employs a small percentage of the available data sample and is optimal in the L2 sense. While only requiring OðN 2 Þ optimization routines to estimate the required kernel weighting coefficients, the proposed method provides similar levels of performance accuracy and sparseness of representation as Support Vector Machine density estimation, which requires OðN 3 Þ optimization routines, and which has previously been shown to consistently outperform Gaussian Mixture Models. It is also demonstrated that the proposed density estimator consistently provides superior density estimates for similar levels of data reduction to that provided by the recently proposed DensityBased Multiscale Data Condensation algorithm and, in addition, has comparable computational scaling. The additional advantage of the proposed method is that no extra free parameters are introduced such as regularization, bin width, or condensation ratios, making this method a very simple and straightforward approach to providing a reduced set density estimator with comparable accuracy to that of the full sample Parzen density estimator. Index Terms—Kernel density estimation, Parzen window, data condensation, sparse representation. 1
Exact conditional tests for crossclassifications: Approximation of attained significance levels
 Psychometrika
, 1979
"... A procedure is proposed for approximating attained significance levels of exact conditional tests. The procedure utilizes a sampling from the null distribution of tables having the same marginal frequencies as the observed table. Application of the approximation through a computer subroutine yields ..."
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Cited by 31 (2 self)
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A procedure is proposed for approximating attained significance levels of exact conditional tests. The procedure utilizes a sampling from the null distribution of tables having the same marginal frequencies as the observed table. Application of the approximation through a computer subroutine yields precise approximations for practically any table dimensions and sample size. Key words: contingency tables, independence, chisquare, KruskalWallis, computer algorithm. 1.
User choices: A new yardstick for the evaluation of ranking algorithms for interactive query expansion
 Information Processing and Management
, 1995
"... AbstractThe performance of eight ranking algorithms was evaluated with respect to their effectiveness in ranking terms for query expansion. The evaluation was conducted within an investigation of interactive query expansion and relevance feedback in a real operational environment. This study focus ..."
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Cited by 24 (0 self)
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AbstractThe performance of eight ranking algorithms was evaluated with respect to their effectiveness in ranking terms for query expansion. The evaluation was conducted within an investigation of interactive query expansion and relevance feedback in a real operational environment. This study focuses on the identification of algorithms that most effectively take cognizance of user preferences. User choices (i.e. the terms selected by the searchers for the query expansion search) provided the yardstick for the evaluation of the eight ranking algorithms. This methodology introduces a useroriented approach in evaluating ranking algorithms for query expansion in contrast to the standard, systemoriented approaches. Similarities in the performance of the eight algorithms and the ways that these algorithms rank terms were the main focus of this evaluation. The findings demonstrate that the rlohi, wpq, emim, and porter algorithms have similar performance in bringing good terms to the top of a ranked list of terms for query expansion. However, further evaluation of the algorithms in different (e.g. fulltext) environments is needed before these results can be generalized beyond the context of the present study. 1.