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High confidence visual recognition of persons by a test of statistical independence
 IEEE TRANS. ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1993
"... A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person’s face is the detailed texture of each eye’s iris: An estimate of its statistical complexity in a sample of the ..."
Abstract

Cited by 601 (8 self)
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imply a theoretical “crossover ” error rate of one in 131000 when a decision criterion is adopted that would equalize the false accept and false reject error rates. In the typical recognition case, given the mean observed degree of iris code agreement, the decision confidence levels correspond formally
What is a hidden Markov model?
, 2004
"... Often, problems in biological sequence analysis are just a matter of putting the right label on each residue. In gene identification, we want to label nucleotides as exons, introns, or intergenic sequence. In sequence alignment, we want to associate residues in a query sequence with homologous resi ..."
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Cited by 1333 (8 self)
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scoring answer is one thing, but what does the score mean, and how confident are we that the best answer, or any given part of it, is correct? A third issue is extensibility. The moment we perfect our ad hoc genefinder, we wish we had also modeled translational initiation consensus, alternative splicing
Probabilistic Mental Models: A Brunswikian Theory of Confidence
 Psychological Review
, 1991
"... Research on people’s confidence in their general knowledge has to date produced two fairly stable effects, many inconsistent results, and no comprehensive theory. We propose such a comprehensive framework, the theory of probabilistic mental models (PMM theory). The theory (a) explains both the overc ..."
Abstract

Cited by 264 (27 self)
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the overconfidence effect (mean confidence is higher than percentage of answers correct) and the hardeasy effect (overconfidence increases with item difficulty) reported in the literature and (b) predicts conditions under which both effects appear, disappear, or invert. In addition, (c) it predicts a new phenomenon
A Procedure for Generating BatchMeans Confidence Intervals for Simulation: Checking Independence and Normality
, 2007
"... Batch means are sample means of subsets of consecutive subsamples from a simulation output sequence. Independent and normally distributed batch means are not only the requirement for constructing a confidence interval for the mean of the steadystate distribution of a stochastic process, but are als ..."
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Cited by 2 (2 self)
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Batch means are sample means of subsets of consecutive subsamples from a simulation output sequence. Independent and normally distributed batch means are not only the requirement for constructing a confidence interval for the mean of the steadystate distribution of a stochastic process
Understanding FaultTolerant Distributed Systems
 COMMUNICATIONS OF THE ACM
, 1993
"... We propose a small number of basic concepts that can be used to explain the architecture of faulttolerant distributed systems and we discuss a list of architectural issues that we find useful to consider when designing or examining such systems. For each issue we present known solutions and design ..."
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Cited by 374 (23 self)
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We propose a small number of basic concepts that can be used to explain the architecture of faulttolerant distributed systems and we discuss a list of architectural issues that we find useful to consider when designing or examining such systems. For each issue we present known solutions and design alternatives, we discuss their relative merits and we give examples of systems which adopt one approach or the other. The aim is to introduce some order in the complex discipline of designing and understanding faulttolerant distributed systems.
Ensemble Tracking
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2007
"... We consider tracking as a binary classification problem, where an ensemble of weak classifiers is trained online to distinguish between the object and the background. The ensemble of weak classifiers is combined into a strong classifier using AdaBoost. The strong classifier is then used to label pi ..."
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Cited by 327 (2 self)
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pixels in the next frame as either belonging to the object or the background, giving a confidence map. The peak of the map, and hence the new position of the object, is found using mean shift. Temporal coherence is maintained by updating the ensemble with new weak classifiers that are trained on
Bayesian Compressive Sensing
, 2007
"... The data of interest are assumed to be represented as Ndimensional real vectors, and these vectors are compressible in some linear basis B, implying that the signal can be reconstructed accurately using only a small number M ≪ N of basisfunction coefficients associated with B. Compressive sensing ..."
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Cited by 324 (24 self)
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the underlying signal f, “error bars ” are also estimated, these giving a measure of confidence in the inverted signal; (ii) using knowledge of the error bars, a principled means is provided for determining when a sufficient
Confidence
, 2008
"... distribution, kNN Confidence measures for kNN classification are an important aspect of building practical systems for online handwritten character recognition. In many cases, the distribution of training samples across the different classes is marked by significant skew, either as a consequence o ..."
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distribution, kNN Confidence measures for kNN classification are an important aspect of building practical systems for online handwritten character recognition. In many cases, the distribution of training samples across the different classes is marked by significant skew, either as a consequence
CONFIDENCE
"... All three articles in my dissertation gather information from individuals, analyze it, and aggregate that information into forecasts of upcoming events. The motivation is to make forecasts more efficient (accurate and timely), more versatile (provide the most useful information for each stakeholder) ..."
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: sample selection (a random sample of representative group versus a selfselected group), question type (intention versus expectation), aggregation method (average versus weighted by money, a proxy for confidence), and incentive (not incentive compatible versus incentive compatible). The second article
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