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Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods
 ADVANCES IN LARGE MARGIN CLASSIFIERS
, 1999
"... The output of a classifier should be a calibrated posterior probability to enable postprocessing. Standard SVMs do not provide such probabilities. One method to create probabilities is to directly train a kernel classifier with a logit link function and a regularized maximum likelihood score. Howev ..."
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Cited by 1051 (0 self)
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The output of a classifier should be a calibrated posterior probability to enable postprocessing. Standard SVMs do not provide such probabilities. One method to create probabilities is to directly train a kernel classifier with a logit link function and a regularized maximum likelihood score
A Note on Platt's Probabilistic Outputs for Support Vector Machines
, 2003
"... Platt's probabilistic outputs for Support Vector Machines [6] has been popular for applications that require posterior class probabilities. In this note, we propose an improvement which theoretically converges and avoids numerical difficulties. A simpler and readytouse pseudo code is includ ..."
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Cited by 190 (5 self)
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Platt's probabilistic outputs for Support Vector Machines [6] has been popular for applications that require posterior class probabilities. In this note, we propose an improvement which theoretically converges and avoids numerical difficulties. A simpler and readytouse pseudo code
Feature Selection via Probabilistic Outputs
"... This paper investigates two featurescoring criteria that make use of estimated class probabilities: one method proposed by Shen et al. (2008) and a complementary approach proposed below. We develop a theoretical framework to analyze each criterion and show that both estimate the spread (across all ..."
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This paper investigates two featurescoring criteria that make use of estimated class probabilities: one method proposed by Shen et al. (2008) and a complementary approach proposed below. We develop a theoretical framework to analyze each criterion and show that both estimate the spread (across all values of a given feature) of the probability that an example belongs to the positive class. Based on our analysis, we predict when each scoring technique will be advantageous over the other and give empirical results validating our predictions. 1.
Pairwise Neural Network Classifiers with Probabilistic Outputs
 in Advances in Neural Information Processing Systems 7
, 1994
"... Multiclass classification problems can be efficiently solved by partitioning the original problem into subproblems involving only two classes: for each pair of classes, a (potentially small) neural network is trained using only the data of these two classes. We show how to combine the outputs of t ..."
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Cited by 33 (0 self)
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Multiclass classification problems can be efficiently solved by partitioning the original problem into subproblems involving only two classes: for each pair of classes, a (potentially small) neural network is trained using only the data of these two classes. We show how to combine the outputs
Technical supplement to “Consistent probabilistic outputs for protein
"... function prediction” ..."
Fitting a mixture model by expectation maximization to discover motifs in biopolymers.
 Proc Int Conf Intell Syst Mol Biol
, 1994
"... Abstract The algorithm described in this paper discovers one or more motifs in a collection of DNA or protein sequences by using the technique of expect~tiou ma.,dmization to fit a twocomponent finite mixture model to the set of sequences. Multiple motifs are found by fitting a mixture model to th ..."
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Cited by 947 (5 self)
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to the data, probabilistically erasing tile occurrences of the motif thus found, and repeating the process to find successive motifs. The algorithm requires only a set of unaligned sequences and a number specifying the width of the motifs as input. It returns a model of each motif and a threshold which
A Smoothed Boosting Algorithm Using Probabilistic Output Codes
 Proc. 22 nd International Conference on Machine Learning
, 2005
"... AdaBoost.OC has shown to be an e#ective method in boosting "weak" binary classifiers for multiclass learning. It employs the Error Correcting Output Code (ECOC) method to convert a multiclass learning problem into a set of binary classification problems, and applies the AdaBoost al ..."
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Cited by 2 (0 self)
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AdaBoost.OC has shown to be an e#ective method in boosting "weak" binary classifiers for multiclass learning. It employs the Error Correcting Output Code (ECOC) method to convert a multiclass learning problem into a set of binary classification problems, and applies the Ada
Algorithmic information theory
 IBM JOURNAL OF RESEARCH AND DEVELOPMENT
, 1977
"... This paper reviews algorithmic information theory, which is an attempt to apply informationtheoretic and probabilistic ideas to recursive function theory. Typical concerns in this approach are, for example, the number of bits of information required to specify an algorithm, or the probability that ..."
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Cited by 385 (18 self)
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This paper reviews algorithmic information theory, which is an attempt to apply informationtheoretic and probabilistic ideas to recursive function theory. Typical concerns in this approach are, for example, the number of bits of information required to specify an algorithm, or the probability
[9] J. Platt, “Probabilistic outputs for SVMs and comparisons to regularized likelihood methods, ” in Advances in Large Margin
"... outputs for pattern recognition using analytical geometry, ” Neurocomputing, ..."
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outputs for pattern recognition using analytical geometry, ” Neurocomputing,
Learning Concepts from LargeScale Data Sets by Pairwise Coupling with Probabilistic Outputs
 IEEE Transactions on Neural Networks
, 1999
"... Abstract — This paper considers the problems of learning concepts from largescale data sets. The way we take is completely classification algorithm independent. Firstly, the original problem is decomposed into a series of smaller twoclass subproblems which are easier to be solved. Secondly we pre ..."
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Cited by 1 (1 self)
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as the minmax principles in the special case considering 01 outputs. We also propose a revised approach which reduces the computational complexity of the training and testing stage to a linear level. Finally, experiments on both the synthetic and textclassification data are demonstrated. The results
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