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The Nature of Statistical Learning Theory
, 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
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Cited by 13236 (32 self)
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Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based
A New Probabilistic Approach for Fractal Based Image Compression
"... “ A New probabilistic approach for fractal based image compression ..."
A new probabilistic approach for the classification of normalised variables
"... While in the classical approach, for a fixed set of p variables, the data represent a sample of n individuals, in our approach, for a given set of n individuals, the data represent now a sample of p variables. We suppose that the p variables randomly selected from a population of variables are norma ..."
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While in the classical approach, for a fixed set of p variables, the data represent a sample of n individuals, in our approach, for a given set of n individuals, the data represent now a sample of p variables. We suppose that the p variables randomly selected from a population of variables
Bisimulation through probabilistic testing
 in “Conference Record of the 16th ACM Symposium on Principles of Programming Languages (POPL
, 1989
"... We propose a language for testing concurrent processes and examine its strength in terms of the processes that are distinguished by a test. By using probabilistic transition systems as the underlying semantic model, we show how a testing algorithm can distinguish, with a probability arbitrarily clos ..."
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Cited by 529 (14 self)
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is shown to identify a new process relation called probabilistic bisimulationwhich is strictly stronger than bisimulation. li? 1991 Academic Press. Inc. 1.
Probabilistic Latent Semantic Analysis
 In Proc. of Uncertainty in Artificial Intelligence, UAI’99
, 1999
"... Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of twomode and cooccurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. Compared to standard Latent Sema ..."
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Cited by 771 (9 self)
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Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of twomode and cooccurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. Compared to standard Latent
Probabilistic Principal Component Analysis
 JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B
, 1999
"... Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based upon a probability model. In this paper we demonstrate how the principal axes of a set of observed data vectors may be determined through maximumlikelihood estimation of paramet ..."
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Cited by 709 (5 self)
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of parameters in a latent variable model closely related to factor analysis. We consider the properties of the associated likelihood function, giving an EM algorithm for estimating the principal subspace iteratively, and discuss, with illustrative examples, the advantages conveyed by this probabilistic approach
Probabilistic Latent Semantic Indexing
, 1999
"... Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized ..."
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Cited by 1225 (10 self)
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Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized
M.: A new probabilistic approach in rank regression with optimal bayesian partitioning
 Journal of Machine Learning Research
, 2007
"... In this paper, we consider the supervised learning task which consists in predicting the normalized rank of a numerical variable. We introduce a novel probabilistic approach to estimate the posterior distribution of the target rank conditionally to the predictors. We turn this learning task into a m ..."
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Cited by 5 (1 self)
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In this paper, we consider the supervised learning task which consists in predicting the normalized rank of a numerical variable. We introduce a novel probabilistic approach to estimate the posterior distribution of the target rank conditionally to the predictors. We turn this learning task into a
New Probabilistic Approach to Estimate Vehicle Failure Trajectories in Curve Driving
, 2013
"... The vehicle trajectories analysis on dangerous bends is an important task to improve road safety. This paper propose a new methodology to predict failure trajectories of light vehicles in curve driving. It consists to use a stochastic modelling and reliability analysis in order to estimate the failu ..."
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to each class of trajectories. Based on the joint use of probabilistic methods for modelling uncertainties, reliability analysis for assessing risk levels and statistics for classifying the trajectories, this approach provides a realistic answer to the tackled problem. Experiments show the relevance
Results 1  10
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194,453