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The Probabilistic Method
, 2007
"... The probabilistic method comes up in various fields in mathematics. In these notes, we will give a brief introduction to graph theory and applications of the probabilistic method in proving bounds for Ramsey numbers and a theorem in graph cuts. This method is based on the following idea: in order to ..."
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The probabilistic method comes up in various fields in mathematics. In these notes, we will give a brief introduction to graph theory and applications of the probabilistic method in proving bounds for Ramsey numbers and a theorem in graph cuts. This method is based on the following idea: in order
Probabilistic Method
, 1994
"... A linear forest is the union of a set of vertex disjoint paths. Akiyama, Exoo and Harary and independently Hilton have conjectured that the edges of every graph of maximum degree ∆ can be covered by d∆+12 e linear forests. We show that almost every graph can be covered with this number of linear for ..."
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A linear forest is the union of a set of vertex disjoint paths. Akiyama, Exoo and Harary and independently Hilton have conjectured that the edges of every graph of maximum degree ∆ can be covered by d∆+12 e linear forests. We show that almost every graph can be covered with this number of linear forests.
Probabilistic Methods for Finding People
 INTERNATIONAL JOURNAL OF COMPUTER VISION
, 2001
"... Finding people in pictures presents a particularly difficult object recognition problem. We show how to find people by finding candidate body segments, and then constructing assemblies of segments that are consistent with the constraints on the appearance of a person that result from kinematic prope ..."
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Cited by 126 (2 self)
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Finding people in pictures presents a particularly difficult object recognition problem. We show how to find people by finding candidate body segments, and then constructing assemblies of segments that are consistent with the constraints on the appearance of a person that result from kinematic properties. Since a reasonable model of a person requires at least nine segments, it is not possible to inspect every group, due to the huge combinatorial complexity. We propose two
Learning probabilistic relational models
 In IJCAI
, 1999
"... A large portion of realworld data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat " data representations. Thus, to apply these methods, we are forced to convert our data into a flat form, thereby losing much ..."
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Cited by 613 (30 self)
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A large portion of realworld data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat " data representations. Thus, to apply these methods, we are forced to convert our data into a flat form, thereby losing much
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
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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results for different types of text and linguistic data collections and discusses an application in automated document indexing. The experiments indicate substantial and consistent improvements of the probabilistic method over standard Latent Semantic Analysis.
PROBABILISTIC METHODS IN GROUP THEORY
, 1965
"... The application of probabilistic methods to another chapter of mathematics (number theory, different branches of analysis, graph theory etc.) has in the last 30 years often led to interesting results, which could not be obtained by the usual methods of the chapters in question. These results are in ..."
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Cited by 29 (0 self)
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The application of probabilistic methods to another chapter of mathematics (number theory, different branches of analysis, graph theory etc.) has in the last 30 years often led to interesting results, which could not be obtained by the usual methods of the chapters in question. These results
On Probabilistic Methods in Fuzzy Theory
"... This lecture is mainly a survey of useful probabilistic methods in the theoretical analysis of fuzzy theory for modeling and design of intelligent systems. The probabilistic methods also are useful for fusing domain knowledge with numerical data in the field of intelligent data analysis. © 2004 Wile ..."
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Cited by 1 (0 self)
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This lecture is mainly a survey of useful probabilistic methods in the theoretical analysis of fuzzy theory for modeling and design of intelligent systems. The probabilistic methods also are useful for fusing domain knowledge with numerical data in the field of intelligent data analysis. © 2004
A Bayesian method for the induction of probabilistic networks from data
 MACHINE LEARNING
, 1992
"... This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computerassisted hypothesis testing, automated scientific discovery, and automated construction of probabili ..."
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Cited by 1400 (31 self)
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This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computerassisted hypothesis testing, automated scientific discovery, and automated construction
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