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classification, and the classspecific feature theorem
 IEEE Trans. Inform. Theory
, 2000
"... Abstract—A new proof of the classspecific feature theorem is given. The proof makes use of the observed data as opposed to the set of sufficient statistics as in the original formulation. We prove the theorem for the classical case, in which the parameter vector is deterministic and known, as well ..."
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Cited by 4 (0 self)
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Abstract—A new proof of the classspecific feature theorem is given. The proof makes use of the observed data as opposed to the set of sufficient statistics as in the original formulation. We prove the theorem for the classical case, in which the parameter vector is deterministic and known, as well
ClassSpecific Feature Sets in Classification
 in Proceedings of the 1998 IEEE International Symposium on Intelligent Control (ISIC
, 1998
"... The commonly used featurebased classifier implements the maximum aposteriori probability (MAP) of the data class given the features. This requires the joint probability density function (PDF) of the features under each of the class hypotheses. Unfortunately, these PDF's are rarely known and mu ..."
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Cited by 7 (0 self)
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and must be estimated from training data. Poor performance results if the amount of training data is insufficient to estimate the highdimensional feature PDF's. The classspecific theorem is presented in which the MAP decision rule is rewritten as a function of lowdimensional PDF's which may
The PDF projection Theorem and the ClassSpecific Method
 IEEE Trans. on Signal Processing
, 2003
"... Abstract—In this paper, we present the theoretical foundation for optimal classification using classspecific features and provide examples of its use. A new probability density function (PDF) projection theorem makes it possible to project probability density functions from a lowdimensional featu ..."
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Cited by 14 (4 self)
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Abstract—In this paper, we present the theoretical foundation for optimal classification using classspecific features and provide examples of its use. A new probability density function (PDF) projection theorem makes it possible to project probability density functions from a low
The PDF Projection Theorem and the ClassSpecific Method
"... Abstract—In this paper, we present the theoretical foundation for optimal classification using classspecific features and provide examples of its use. A new probability density function (PDF) projection theorem makes it possible to project probability density functions from a lowdimensional featur ..."
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Abstract—In this paper, we present the theoretical foundation for optimal classification using classspecific features and provide examples of its use. A new probability density function (PDF) projection theorem makes it possible to project probability density functions from a low
classspecific antibodies to native and denatured DNA in man
"... SUMMARY Enzymelinked immunosorbent assays (ELISA) have been set up for determination of plasma IgG and IgM antibodies to native (n) and denatured (d) DNA. Normal male and female donors generally gave low values in the assays for IgG; IgM control values were higher, particularly in females. Mean val ..."
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of the IgG antibodies determined in the 'native ' assay were able to bind to nDNA and dDNA with comparable avidity, whereas most of those reacting in the 'denatured ' assay could only bind dDNA. The former antibodies were probably directed against shared determinants on the deoxyribose
Online Adaption of Classspecific Codebooks for Instance Tracking
, 2010
"... Offline trained classspecific object detectors are designed to detect any instance of the class in a given image or video sequence. In the context of object tracking, however, one seeks the location and scale of a target object, which is a specific instance of the class. Hence, the target needs to ..."
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Cited by 18 (3 self)
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Offline trained classspecific object detectors are designed to detect any instance of the class in a given image or video sequence. In the context of object tracking, however, one seeks the location and scale of a target object, which is a specific instance of the class. Hence, the target needs
Classspecific quality of service guarantees in multimedia communication networks
, 1999
"... An admission control approach that can provide per class packet loss and delay Quality of Service guarantees is developed. The proposed approach is based on large deviations performance analysis results. We consider the problem of qualityofservice (QoS) provisioning in modern highspeed, multimedi ..."
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Cited by 11 (1 self)
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speed, multimedia, communication networks. We quantify QoS by the probabilities of loss and excessive delay of an arbitrary packet, and introduce the model of a multiclass node (switch) which provides network access to users that may belong to multiple service classes. We treat such a node as a stochastic system
GALL et al.: ONLINE ADAPTION OF CLASSSPECIFIC CODEBOOKS 1 Online Adaption of Classspecific Codebooks for Instance Tracking
"... Offline trained classspecific object detectors are designed to detect any instance of the class in a given image or video sequence. In the context of object tracking, however, one seeks the location and scale of a target object, which is a specific instance of the class. Hence, the target needs to ..."
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Offline trained classspecific object detectors are designed to detect any instance of the class in a given image or video sequence. In the context of object tracking, however, one seeks the location and scale of a target object, which is a specific instance of the class. Hence, the target needs
A MULTIRESOLUTION HIDDEN MARKOV MODEL USING CLASSSPECIFIC FEATURES
"... We address the problem in signal classification applications, such as automatic speech recognition (ASR) systems that employ the hidden Markov model (HMM), that it is necessary to settle for a fixed analysis window size and a fixed feature set. This is despite the fact that complex signals such as h ..."
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Cited by 1 (1 self)
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such as human speech typically contain a wide range of signal types and durations. We apply the probability density function (PDF) projection theorem to generalize the hidden Markov model (HMM) to utilize a different features and segment length for each state. We demonstrate the algorithm using speech analysis
Controlling True Positive Rate in ROC Analysis
"... ROC analysis is a widely used method for evaluating the performance of classifiers. In analysis involving scarce data sets leaveoneout resampling techniques might be appropriate. This introduces a problem in terms of computing average ROC curves necessary to determine variance in the true positi ..."
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positive and negative rates. A method to determine decision regions for a specified true positive rate is presented. The method is based on estimating the class specific probability density functions for the two classes. The functions are discretised. Dividing these yields a function where values above
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