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Resource Management for Chained Binary Classifiers
"... Networks of classifiers are capturing the attention of system and algorithmic researchers because they offer improved accuracy over single model classifiers, can be distributed over a network of servers for improved scalability, and can be adapted to available system resources. However, resource man ..."
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the problem of optimal resource allocation for a chain of binary classifiers under generic resource constraints. We formally define a performance measure for the output of a chain of classifiers. We present our results for stateoftheart classifiers operating on telephony data and offer interesting future
Combined Binary Classifiers With Applications To Speech Recognition
 NEARESTNEIGHBOR ECOC WITH APPLICATION TO ALLPAIRS MULTICLASS SVM
, 2002
"... Many applications require classification of examples into one of several classes. A common way of designing such classifiers is to determine the class based on the outputs of several binary classifiers. We consider some of the most popular methods for combining the decisions of the binary classifier ..."
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Cited by 8 (2 self)
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Many applications require classification of examples into one of several classes. A common way of designing such classifiers is to determine the class based on the outputs of several binary classifiers. We consider some of the most popular methods for combining the decisions of the binary
COMBINED BINARY CLASSIFIERS WITH APPLICATIONS TO SPEECH RECOGNITION
"... Many applications require classification of examples into one of several classes. A common way of designing such classifiers is to determine the class based on the outputs of several binary classifiers. We consider some of the most popular methods for combining the decisions of the binary classifier ..."
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Many applications require classification of examples into one of several classes. A common way of designing such classifiers is to determine the class based on the outputs of several binary classifiers. We consider some of the most popular methods for combining the decisions of the binary
The VC Dimension for Mixtures of Binary Classifiers
"... The mixturesofexperts (ME) methodology provides a tool of classification when experts of logistic regression models or Bernoulli models are mixed according to a set of local weights. We show that the VapnikChervonenkis (VC) dimension of the mixturesofexperts architecture is bounded below by ..."
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Introduction The VapnikChervonenkis (VC) dimension is a central concept for recent developments of computational learning theory (see Anthony and Biggs 1992). The VC dimension is a combinatorial parameter defined on a set of binary functions 1 or a system of classifiers, which shows the expressive power
Pn learning: Bootstrapping binary classifiers by structural constraints
 In IEEE Conference on Computer Vision and Pattern Recognition
, 2010
"... This paper shows that the performance of a binary classifier can be significantly improved by the processing of structured unlabeled data, i.e. data are structured if knowing the label of one example restricts the labeling of the others. We propose a novel paradigm for training a binary classifier f ..."
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Cited by 143 (4 self)
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This paper shows that the performance of a binary classifier can be significantly improved by the processing of structured unlabeled data, i.e. data are structured if knowing the label of one example restricts the labeling of the others. We propose a novel paradigm for training a binary classifier
Onthefly Domain Adaptation of Binary Classifiers
"... This work considers the onthefly domain adaptation of supervised binary classifiers, learned offline, in order to adapt them to a target context. The probability density functions associated to negative and positive classes are supposed to be mixtures of the source distributions. Moreover, the mi ..."
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Cited by 1 (1 self)
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This work considers the onthefly domain adaptation of supervised binary classifiers, learned offline, in order to adapt them to a target context. The probability density functions associated to negative and positive classes are supposed to be mixtures of the source distributions. Moreover
Text Dependent Speaker Verifiation Using Binary Classifiers
, 1997
"... This paper describes how a speaker verification task can be advantageously decomposed into a series of binary classification problems, i.e. each problem discriminating between two classes only. Each binary classifier is specific to one speaker, one antispeaker and one word. Decision trees dealing w ..."
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This paper describes how a speaker verification task can be advantageously decomposed into a series of binary classification problems, i.e. each problem discriminating between two classes only. Each binary classifier is specific to one speaker, one antispeaker and one word. Decision trees dealing
Learning Binary Classifiers for MultiClass Problem
, 2006
"... One important idea for the multiclass classification problem is to combine binary classifiers (base classifiers), which is summarized as error correcting output codes (ECOC), and the generalized BradleyTerry (GBT) model gives a method to estimate the multiclass probability. In this memo, we revie ..."
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One important idea for the multiclass classification problem is to combine binary classifiers (base classifiers), which is summarized as error correcting output codes (ECOC), and the generalized BradleyTerry (GBT) model gives a method to estimate the multiclass probability. In this memo, we
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2000
"... We present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a marginbased binary learning algorithm. The proposed framework unifies some of the most popular approaches in which each class ..."
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Cited by 555 (20 self)
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is compared against all others, or in which all pairs of classes are compared to each other, or in which output codes with errorcorrecting properties are used. We propose a general method for combining the classifiers generated on the binary problems, and we prove a general empirical multiclass loss bound
Results 1  10
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