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Document Classification Using a Finite Mixture Model
[article]

1997
*
arXiv
*
pre-print

We define for each

arXiv:cmp-lg/9705005v1
fatcat:ybrkovlmwfhyvketkeifde5aqe
*document*category*a**finite**mixture**model*, which is*a*linear combination of the probability distributions of the clusters. ... We thereby treat the problem of classifying*documents*as that of conducting statistical hypothesis testing over*finite**mixture**models*. ... The primary contribution of this research is that we have proposed the*use*of the*finite**mixture**model*in*document**classification*. 2. ...##
###
Document classification using a finite mixture model

1997
*
Proceedings of the 35th annual meeting on Association for Computational Linguistics -
*

We propose

doi:10.3115/976909.979623
dblp:conf/acl/LiY97
fatcat:fgzngie3p5czjavdce2xgmvw7u
*a*new method of classifying*documents*into categories. We define for each category*a**finite**mixture**model*based on soft clustering of words. ... We treat the problem of classifying*documents*as that of conducting statistical hypothesis testing over*finite**mixture**models*, and employ the EM algorithm to efficiently estimate parameters in*a**finite*... The primary contribution of this research is that we have proposed the*use*of the*finite**mixture**model*in*document**classification*. 2. ...##
###
Document classification using a finite mixture model

1997
*
Proceedings of the eighth conference on European chapter of the Association for Computational Linguistics -
*
unpublished

We propose

doi:10.3115/979617.979623
fatcat:crtiexqsvbh27gopxtdyjjk6ou
*a*new method of classifying*documents*into categories. We define for each category*a**finite**mixture**model*based on soft clustering of words. ... We treat the problem of classifying*documents*as that of conducting statistical hypothesis testing over*finite**mixture**models*, and employ the EM algorithm to efficiently estimate parameters in*a**finite*... The primary contribution of this research is that we have proposed the*use*of the*finite**mixture**model*in*document**classification*. 2. ...##
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Structural poisson mixtures for classification of documents

2008
*
Pattern Recognition (ICPR), Proceedings of the International Conference on
*

Considering the statistical text

doi:10.1109/icpr.2008.4761669
dblp:conf/icpr/GrimNS08
fatcat:2hqqb7lpgbhvfmopty4gdu67ye
*classification*problem we approximate class-conditional probability distributions by structurally modified Poisson*mixtures*. ... By introducing the structural*model*we can*use*different subsets of input variables to evaluate conditional probabilities of different classes in the Bayes formula. ... Statistical*Document**Classification*We assume that after standard preprocessing*a*text*document*d is reduced to*a**finite*list of terms from*a*given vocabulary V d = w i1 , . . . , w i k , w i l ∈ V = { ...##
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Unsupervised Selection and Discriminative Estimation of Orthogonal Gaussian Mixture Models for Handwritten Digit Recognition

2009
*
2009 10th International Conference on Document Analysis and Recognition
*

The problem of determining the appropriate number of components is important in

doi:10.1109/icdar.2009.44
dblp:conf/icdar/ChenLJ09
fatcat:oxwbehbnmvfhfc5qxw3ppv5boa
*finite**mixture**modeling*for pattern*classification*. ... This paper considers the application of an unsupervised clustering method called AutoClass to training of Orthogonal Gaussian*Mixture**Models*(OGMM). ... To our best knowledge, it is the first work of*using*AutoClass for*finite**mixture**model*selection in*document*analysis and recognition. ...##
###
A Generative Model for Self/Non-self Discrimination in Strings
[chapter]

2009
*
Lecture Notes in Computer Science
*

We extend the probabilistic approach to binary

doi:10.1007/978-3-642-04921-7_30
fatcat:7khvyg6o3zfpxfh3aca3blgwcm
*classification*from fixed-length binary strings into variable-length strings from*a**finite*symbol alphabet by fitting*a**mixture**model*of multinomial distributions ...*A*statistical generative*model*is presented as an alternative to negative selection in anomaly detection of string data. ...*Finite*Bernoulli*mixture**models*Stibor [4] presented the*use*of*finite*multivariate Bernoulli*mixtures*as*a*generative*model*for discriminating self and non-self in l-dimensional bit strings. ...##
###
Label Correlation Mixture Model: A Supervised Generative Approach to Multilabel Spoken Document Categorization

2015
*
IEEE Transactions on Emerging Topics in Computing
*

This paper proposes

doi:10.1109/tetc.2014.2377559
fatcat:d6gcguctpjgfnmwivjwlevfgim
*a*novel probabilistic generative*model*, label correlation*mixture**model*(LCMM), to depict the multiply labeled*documents*, which can be*used*for multilabel spoken*document*categorization ... The multilabel conditioned*document**model*can be regarded as*a*supervised label*mixture**model*, in which labels for*a**document*are known. Each label is characterized by distributions over words. ... Therefore, the*document**model*can be regarded as*a*supervised label*mixture**model*. ...##
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Markov Chain Monte Carlo-Based Bayesian Inference for Learning Finite and Infinite Inverted Beta-Liouville Mixture Models

2021
*
IEEE Access
*

THE

doi:10.1109/access.2021.3078670
fatcat:svqp6qn3nvaubefrjrga4wty4m
*FINITE*IBL*MIXTURE**MODEL*Let Y = ( Y 1 , . . . , Y N ) be*a*set of N vectors that represent for example images or*documents*features. ...*FINITE*INVERTED BETA-LIOUVILLE*MIXTURE**MODEL*We start this section by presenting the Liouville family of distributions and the inverted Beta-Liouville distribution, then we propose*a**finite**mixture*based ...##
###
Multi-view EM Algorithm for Finite Mixture Models
[chapter]

2005
*
Lecture Notes in Computer Science
*

In this paper, Multi-View Expectation and Maximization algorithm for

doi:10.1007/11551188_45
fatcat:eeqpkshdf5az5jwuwso2usjipe
*finite**mixture**models*is proposed by*us*to handle realworld learning problems which have natural feature splits. ... EM has been widely*used*in the parameter estimation of*finite**mixture**models*. ...*Finite**mixture**models*and EM algorithm It is said*a*d-dimensional random variable x = [x 1 , x 2 , · · · , x d ] T follows*a*kcomponent*finite**mixture*distribution, if its probability density function ...##
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Page 50 of Educational and Psychological Measurement Vol. 70, Issue 1
[page]

2010
*
Educational and Psychological Measurement
*

For

*finite**mixture**modeling*, the relationship is clear: Initial misclassification of groups has no effect on*classification*accuracy, as FMM does not*use*in any way initial knowledge of group status. ... One of the most important findings of the study is the direction of misclassifi- cation for*finite**mixture**modeling*and DFA. ...##
###
Sense Cluster Based Categorization and Clustering of Abstracts
[chapter]

2006
*
Lecture Notes in Computer Science
*

This paper focuses on the

doi:10.1007/11671299_56
fatcat:laxd2qcggff5rcdtwuqfu4vcfy
*use*of sense clusters for*classification*and clustering of very short texts such as conference abstracts. ... In the case of conference abstracts, all the*documents*are from*a*narrow domain (i.e., share*a*similar terminology), that increases the difficulty of the task. ... Bernoulli*Mixture*-Based Classifiers*A**finite**mixture**model*is*a*probability (density) function of the form: p(x) = I i=1 p(i)p(x|i) ( 1 ) where I is the number of*mixture*components and, for each component ...##
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Analyzing different functional forms of the varying weight parameter for finite mixture of negative binomial regression models

2014
*
Analytic Methods in Accident Research
*

Therefore, when

doi:10.1016/j.amar.2013.11.001
fatcat:ahonewqtbnabzp2zzvrcmflomi
*using*the*finite**mixture*of NB*models*with varying weight parameters to analyze the crash data, it is suggested that transportation safety analysts should include*Model*5 (which*models*... Previously, the weight parameter of the*finite**mixture*of regression*models*has been assumed to be invariant of the characteristics of the observations under study. ... As*documented*in Park et al. (7) , the*finite**mixture**model*has two advantages over the traditional NB regression*model*. ...##
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Online Learning of a Dirichlet Process Mixture of Generalized Dirichlet Distributions for Simultaneous Clustering and Localized Feature Selection

2012
*
Journal of machine learning research
*

In this paper, we propose

dblp:journals/jmlr/FanB12
fatcat:tcwnvx2atfflbcyew4dkqwpl54
*a*novel online clustering approach based on*a*Dirichlet process*mixture*of generalized Dirichlet (GD) distributions, which can be considered as an extension of the*finite*GD*mixture*... By learning the proposed*model*in an online manner*using**a*variational approach, all the involved parameters and features saliencies are estimated simultaneously and effectively in closed forms. ...*Model*Specification*Finite*GD*Mixture*with Localized Feature Selection Suppose that we have*a*D-dimensional random vector Y = (Y 1 , . . . , Y D ) which is drawn from*a**finite**mixture*of generalized ...##
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Dirichlet-vMF Mixture Model
[article]

2017
*
arXiv
*
pre-print

This

arXiv:1702.07495v1
fatcat:k447sgrhkve2rjek4hibxdn324
*document*is about the multi-*document*Von-Mises-Fisher*mixture**model*with*a*Dirichlet prior, referred to as VMFMix. ... The performance of VMFMix on two*document**classification*tasks is reported, with some preliminary analysis. ... Here α is*a*hyperparameter, {µ k , κ k } are parameters of*mixture*components to be learned. ...##
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Multidimensional Membership Mixture Models
[article]

2012
*
arXiv
*
pre-print

They are built upon Dirichlet process

arXiv:1208.0402v1
fatcat:t4z7vp4mhrhg5k7zklpicvgrqy
*mixture**models*, latent Dirichlet allocation, and*a*combination respectively. ... Our experiments show that our M3*models*achieve better performance*using*fewer topics than many classic topic*models*. ...*Finite*M 3*Models*for Topic*Modeling*Latent Dirichlet allocation (LDA) employs*a*hierarchical*finite**mixture**model*to describe the generative process of*a**document*: first,*a*topic proportion π over K topics ...
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