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15,051
Probabilistic approaches to rough sets
 Expert Systems
, 2003
"... This paper reviews probabilistic approaches to rough sets in granulation, approximation, and rule induction. The Shannon entropy function is used to quantitatively characterize partitions of a universe. Both algebraic and probabilistic rough set approximations are studied. The probabilistic approxim ..."
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Cited by 22 (10 self)
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This paper reviews probabilistic approaches to rough sets in granulation, approximation, and rule induction. The Shannon entropy function is used to quantitatively characterize partitions of a universe. Both algebraic and probabilistic rough set approximations are studied. The probabilistic
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 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
Images Fusion with Probabilistic Approach
"... In this paper we present a method for image fusion based on a probabilistic approach. The objective is to obtain a segmented image from two images representing the same scene captured at the same moment. Image resulting could then be used to estimate finer. ..."
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In this paper we present a method for image fusion based on a probabilistic approach. The objective is to obtain a segmented image from two images representing the same scene captured at the same moment. Image resulting could then be used to estimate finer.
A Probabilistic Approach to Semantic Representation
, 2002
"... Semantic networks produced from human data have statistical properties that cannot be easily captured by spatial representations. We explore a probabilistic approach to semantic representation that explicitly models the probability with which words occur in different contexts, and hence captures the ..."
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Cited by 77 (5 self)
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Semantic networks produced from human data have statistical properties that cannot be easily captured by spatial representations. We explore a probabilistic approach to semantic representation that explicitly models the probability with which words occur in different contexts, and hence captures
Using a Probabilistic Approach ∗
, 2011
"... Intravascular ultrasound (IVUS) is a catheterbased medical imaging technique that produces crosssectional images of blood vessels and is particularly useful for studying atherosclerosis. In this paper, we present a probabilistic approach for the identification of the luminal border in IVUS images. ..."
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Intravascular ultrasound (IVUS) is a catheterbased medical imaging technique that produces crosssectional images of blood vessels and is particularly useful for studying atherosclerosis. In this paper, we present a probabilistic approach for the identification of the luminal border in IVUS images
Probabilistic Approach to Cinderella Problem
, 2012
"... This short paper serves as a probabilistic approach to one of the classical problems in marriage. While this approach is more generally referred as the Secretary Problem, we apply it in woman’s point of view in accepting proposals with additional assumptions. Consider a pool of n men proposing to a ..."
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This short paper serves as a probabilistic approach to one of the classical problems in marriage. While this approach is more generally referred as the Secretary Problem, we apply it in woman’s point of view in accepting proposals with additional assumptions. Consider a pool of n men proposing to a
Unsupervised Learning by Probabilistic Latent Semantic Analysis
 Machine Learning
, 2001
"... Abstract. This paper presents a novel statistical method for factor analysis of binary and count data which is closely related to a technique known as Latent Semantic Analysis. In contrast to the latter method which stems from linear algebra and performs a Singular Value Decomposition of cooccurren ..."
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Cited by 618 (4 self)
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occurrence tables, the proposed technique uses a generative latent class model to perform a probabilistic mixture decomposition. This results in a more principled approach with a solid foundation in statistical inference. More precisely, we propose to make use of a temperature controlled version of the Expectation
Fast probabilistic algorithms for verification of polynomial identities
 J. ACM
, 1980
"... ABSTRACT The starthng success of the RabmStrassenSolovay pnmahty algorithm, together with the intriguing foundattonal posstbthty that axtoms of randomness may constttute a useful fundamental source of mathemaucal truth independent of the standard axmmaUc structure of mathemaUcs, suggests a wgorous ..."
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Cited by 520 (1 self)
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and Sturm sequences are given. Probabilistlc calculatton in real anthmetlc, prewously considered by Davis, is justified ngorously, but only in a special case. Theorems of elementary geometry can be proved much more efficiently by the techmques presented than by any known arttficmlmtelhgence approach
Probabilistic approach to inverse problems
 in International Handbook of Earthquake & Engineering Seismology, Part A
, 2002
"... In ‘inverse problems ’ data from indirect measurements are used to estimate unknown parameters of physical systems. Uncertain data, (possibly vague) prior information on model parameters, and a physical theory relating the model parameters to the observations are the fundamental elements of any inve ..."
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Cited by 24 (0 self)
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In ‘inverse problems ’ data from indirect measurements are used to estimate unknown parameters of physical systems. Uncertain data, (possibly vague) prior information on model parameters, and a physical theory relating the model parameters to the observations are the fundamental elements of any inverse problem. Using concepts from probability theory, a consistent formulation of inverse problems can be made, and, while the most general solution of the inverse problem requires extensive use of Monte Carlo methods, special hypotheses (e.g., Gaussian uncertainties) allow, in some cases, an analytical solution to part of the problem (e.g., using the method of least squares).
Results 11  20
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