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Multinomial Naive Bayes for Text Categorization Revisited
 In: Lecture Notes in Computer Science
, 2005
"... Abstract. This paper presents empirical results for several versions of the multinomial naive Bayes classifier on four text categorization problems, and a way of improving it using locally weighted learning. More specifically, it compares standard multinomial naive Bayes to the recently proposed tr ..."
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shows that support vector machines can, in fact, sometimes very significantly outperform both methods. Finally, it shows how the performance of multinomial naive Bayes can be improved using locally weighted learning. However, the overall conclusion of our paper is that support vector machines are still
Image Retrieval with Multinomial Relevance Feedback
"... We consider contentbased image retrieval when the user is unable to specify the required content through tags or other explicit properties of the images. In this type of scenario the system must extract information from the user through limited feedback. We consider a protocol that operates through ..."
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Cited by 2 (2 self)
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through a sequence of rounds in each of which a set of k images is displayed and the user must indicate which is closest to their target. Performance is assessed by (1) the number of rounds needed
Detecting Deviation in Multinomially Distributed Data
"... Multinomial models are used in describing the distribution of categorial or discrete variables. In practice we are often interested whether a given sample deviates significantly from a certain multinomial distribution. To determine this, often Pearson’s χ2 or likelihood ratio tests are used. Besid ..."
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Multinomial models are used in describing the distribution of categorial or discrete variables. In practice we are often interested whether a given sample deviates significantly from a certain multinomial distribution. To determine this, often Pearson’s χ2 or likelihood ratio tests are used
Automatic Derivation of the Multinomial PCA Algorithm
 Available at
, 2003
"... Machine learning has reached a point where probabilistic methods can be understood as variations, extensions, and combinations of a small set of abstract themes, e.g., as di#erent instances of the EM algorithm, or as exponential family methods. This allows the automatic derivation of algorithm ..."
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of algorithms customized for di#erent models. One interesting new model is a multinomial version of PCA which has received attention due to its ability to better model documents as having multiple topics.
Localized Smoothing for Multinomial Language Models
, 2000
"... We explore a formal approach to dealing with the zero frequency problem that arises in applications of probabilistic models to language. In this report we introduce the zero frequency problem in the context of probabilistic language models, describe several popular solutions, and introduce localized ..."
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localized smoothing, a potentially better alternative. We formulate localized smoothing as a twostep maximization process, outline the estimation details for both steps and present the experiments which show the technique to have potential for improving performance. 1 Overview Language modeling
Multinomial Representation of Majority Logic Coding
"... Multinomial representations are derived for majority logic operations on bipolar binary data. The coefficients are given simply in terms of the readily computed lower Cholesky factor of Pascal Matrices of order n for codes of block length n. I. ..."
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Multinomial representations are derived for majority logic operations on bipolar binary data. The coefficients are given simply in terms of the readily computed lower Cholesky factor of Pascal Matrices of order n for codes of block length n. I.
Multinomial . . .  Selection, Classification and Subcategory Data
, 2010
"... In probability and statistics, uncertainty is usually quantified using singlevalued probabilities satisfying Kolmogorov’s axioms. Generalisation of classical probability theory leads to various less restrictive representations of uncertainty which are collectively referred to as imprecise probabili ..."
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probability. Several approaches to statistical inference using imprecise probability have been suggested, one of which is nonparametric predictive inference (NPI). The multinomial NPI model was recently proposed [14,17], which quantifies uncertainty in terms of lower and upper probabilities. It has several
Approximate Inference for the Multinomial Logit Models
 Statistics and Probability Letters
, 2009
"... Higher order asymptotic theory is used to derive pvalues that achieve superior accuracy compared to the pvalues obtained from traditional tests for inference about parameters of the multinomial logit model. Simulations are provided to assess the finite sample behavior of the test statistics consid ..."
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Higher order asymptotic theory is used to derive pvalues that achieve superior accuracy compared to the pvalues obtained from traditional tests for inference about parameters of the multinomial logit model. Simulations are provided to assess the finite sample behavior of the test statistics
Variational Multinomial Logit Gaussian Process
"... Gaussian process prior with an appropriate likelihood function is a flexible nonparametric model for a variety of learning tasks. One important and standard task is multiclass classification, which is the categorization of an item into one of several fixed classes. A usual likelihood function for ..."
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for this is the multinomial logistic likelihood function. However, exact inference with this model has proved to be difficult because highdimensional integrations are required. In this paper, we propose a variational approximation to this model, and we describe the optimization of the variational parameters. Experiments
Testing for the Supremacy of a Multinomial Cell
, 2009
"... Tests for the supremacy of a multinomial cell probability are developed. The tested null hypothesis states that a particular cell of interest is not more probable than all others. Rejection of this null leads to the conclusion that the cell of interest has a strictly greater probability than all oth ..."
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Tests for the supremacy of a multinomial cell probability are developed. The tested null hypothesis states that a particular cell of interest is not more probable than all others. Rejection of this null leads to the conclusion that the cell of interest has a strictly greater probability than all
Results 21  30
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37,774