Document Classification Using a Finite Mixture Model

We define for each 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. ...

arXiv:cmp-lg/9705005v1 fatcat:ybrkovlmwfhyvketkeifde5aqe

We propose 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. ...

doi:10.3115/976909.979623 dblp:conf/acl/LiY97 fatcat:fgzngie3p5czjavdce2xgmvw7u

We propose 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. ...

doi:10.3115/979617.979623 fatcat:crtiexqsvbh27gopxtdyjjk6ou

Considering the statistical text 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 = { ...

doi:10.1109/icpr.2008.4761669 dblp:conf/icpr/GrimNS08 fatcat:2hqqb7lpgbhvfmopty4gdu67ye

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

doi:10.1109/icdar.2009.44 dblp:conf/icdar/ChenLJ09 fatcat:oxwbehbnmvfhfc5qxw3ppv5boa

We extend the probabilistic approach to binary 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. ...

doi:10.1007/978-3-642-04921-7_30 fatcat:7khvyg6o3zfpxfh3aca3blgwcm

This paper proposes 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. ...

doi:10.1109/tetc.2014.2377559 fatcat:d6gcguctpjgfnmwivjwlevfgim

THE 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 ...

doi:10.1109/access.2021.3078670 fatcat:svqp6qn3nvaubefrjrga4wty4m

DOAJ

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

doi:10.1007/11551188_45 fatcat:eeqpkshdf5az5jwuwso2usjipe

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. ...

This paper focuses on the 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 ...

doi:10.1007/11671299_56 fatcat:laxd2qcggff5rcdtwuqfu4vcfy

Therefore, when 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. ...

doi:10.1016/j.amar.2013.11.001 fatcat:ahonewqtbnabzp2zzvrcmflomi

In this paper, we propose 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 ...

dblp:journals/jmlr/FanB12 fatcat:tcwnvx2atfflbcyew4dkqwpl54

Szczepanski

This 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. ...

arXiv:1702.07495v1 fatcat:k447sgrhkve2rjek4hibxdn324

They are built upon Dirichlet process 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 ...

arXiv:1208.0402v1 fatcat:t4z7vp4mhrhg5k7zklpicvgrqy

Document Classification Using a Finite Mixture Model [article]

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Document classification using a finite mixture model

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Document classification using a finite mixture model

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Structural poisson mixtures for classification of documents

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Unsupervised Selection and Discriminative Estimation of Orthogonal Gaussian Mixture Models for Handwritten Digit Recognition

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A Generative Model for Self/Non-self Discrimination in Strings [chapter]

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Label Correlation Mixture Model: A Supervised Generative Approach to Multilabel Spoken Document Categorization

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Markov Chain Monte Carlo-Based Bayesian Inference for Learning Finite and Infinite Inverted Beta-Liouville Mixture Models

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Analyzing different functional forms of the varying weight parameter for finite mixture of negative binomial regression models

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