Relevant bibliographies by topics / Multinomial naïve bayesian

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  2. Dissertations / Theses
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Academic literature on the topic 'Multinomial naïve bayesian'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Multinomial naïve bayesian"

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Xu, Shuo. "Bayesian Naïve Bayes classifiers to text classification." Journal of Information Science 44, no. 1 (2016): 48–59. http://dx.doi.org/10.1177/0165551516677946.

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Text classification is the task of assigning predefined categories to natural language documents, and it can provide conceptual views of document collections. The Naïve Bayes (NB) classifier is a family of simple probabilistic classifiers based on a common assumption that all features are independent of each other, given the category variable, and it is often used as the baseline in text classification. However, classical NB classifiers with multinomial, Bernoulli and Gaussian event models are not fully Bayesian. This study proposes three Bayesian counterparts, where it turns out that classica
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Olanrewaju, Rasaki Olawale, Sodiq Adejare Olanrewaju, and Lukman Abiodun Nafiu. "Multinomial Naïve Bayes Classifier: Bayesian versus Nonparametric Classifier Approach." European Journal of Statistics 2 (February 22, 2022): 8. http://dx.doi.org/10.28924/ada/stat.2.8.

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This paper proposes a Naïve Bayes Classifier for Bayesian and nonparametric methods of analyzing multinomial regression. The Naïve Bayes classifier adopted Bayes’ rule for solving the posterior of the multinomial regression via its link function known as Logit link. The nonparametric adopted Gaussian, bi-weight kernels, Silverman’s rule of thumb bandwidth selector, and adjusted bandwidth as kernel density estimation. Three categorical responses of information on 78 people using one of three diets (Diet A, B, and C) that consist of scaled variables: age (in years), height (in cm), weight (in kg
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Yashvi, Vaghasiya, Vora Diya, Nehayadav, and Rana Manish. "Language detection using multinomial naïve bayes algorithm." i-manager's Journal on Computer Science 10, no. 2 (2022): 34. http://dx.doi.org/10.26634/jcom.10.2.19014.

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In this multilingual world, automatic detection of written or spoken language using Language Identification (LID) technology is a boon in the global communication with people using different languages in different countries. For simplicity and for the purpose of this research, the process of automatically identifying the language(s) from a document is thought of as LID. Lot of ongoing research projects are in the field of Natural Language Processing (NLP) that uses LID as a part of NLP. This field exploits several algorithms evolved in the field of computer science, individually or in combinat
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Liu, Kuan-Liang, and Tzu-Tsung Wong. "Naïve Bayesian Classifiers with Multinomial Models for rRNA Taxonomic Assignment." IEEE/ACM Transactions on Computational Biology and Bioinformatics 10, no. 5 (2013): 1. http://dx.doi.org/10.1109/tcbb.2013.114.

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Saxena, Neeraj, Ruiyang Wang, Vinayak V. Dixit, and S. Travis Waller. "Frequentist and Bayesian Approaches for Understanding Route Choice of Drivers under Stop-and-Go Traffic." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 9 (2020): 371–82. http://dx.doi.org/10.1177/0361198120929332.

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Driving in congested traffic is a nuisance that not only results in longer travel times, but also triggers frustration and impatience among drivers. A few studies have modeled the effects of congested traffic in the resulting route choice behavior of car drivers. The studies used frequentist models such as discrete choice models to analyze large samples. However, these studies did not compare the inferences obtained from the frequentist and Bayesian approaches, particularly for datasets which are not sufficiently large. It has been shown by researchers that Bayesian models perform well, especi
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Chen, Yilin. "Comparative Analysis of Bayesian Approaches and Variant Methods in the Financial Field." Journal of Education, Humanities and Social Sciences 49 (April 15, 2025): 59–66. https://doi.org/10.54097/38mmdr81.

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It is important for researchers to select the most appropriate model for specific financial tasks. This comparative analysis describes the strengths, limitations, and trade-offs of Bayesian approaches and variant methods. Moreover, this study aims to compare Bayesian approaches and their variant methods in the financial field and assess their effectiveness and relevancy for stock price prediction. This study starts with a brief overview of Bayes’ Theorem, Naïve Bayes (NB), multinomial Naïve Bayes (NBM), and Gaussian Naïve Bayes (GNB). In the prediction of the Brazilian stock market, NBM demons
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Wong, Tzu-Tsung, and Hsing-Chen Tsai. "Multinomial naïve Bayesian classifier with generalized Dirichlet priors for high-dimensional imbalanced data." Knowledge-Based Systems 228 (September 2021): 107288. http://dx.doi.org/10.1016/j.knosys.2021.107288.

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Wong, Tzu-Tsung. "Generalized Dirichlet priors for Naïve Bayesian classifiers with multinomial models in document classification." Data Mining and Knowledge Discovery 28, no. 1 (2012): 123–44. http://dx.doi.org/10.1007/s10618-012-0296-4.

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Atoyebi, Temitope Olufunmi, Rashidah Funke Olanrewaju, N. V. Blamah, and Morufu Olalere. "Malaria Disease Prediction and Grading System: A Performance Model of Multinomial Naïve Bayes (MNB) Machine Learning in Nigerian Hospitals." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 2113–24. http://dx.doi.org/10.22214/ijraset.2023.56378.

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bstract: Malaria disease is the number one cause of death all over the Sub-Sahara world. Data mining can help extract valuable knowledge from available data in the healthcare sector. This allows training a patient health prediction model faster than in a clinical trial. Various implementation of machine learning algorithms such as Bayesian Theorem, Logistic Regression, K-Nearest Neighbor, Support Vector Machine and Multinomial Naïve Bayes (MNB), etc. have been applied on Public Hospital Malaria Disease datasets but there has been a limit to modeling using Multinomial Naïve Bayes Algorithm. Thi
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Wong, Tzu-Tsung, and Chao-Rui Liu. "An efficient parameter estimation method for generalized Dirichlet priors in naïve Bayesian classifiers with multinomial models." Pattern Recognition 60 (December 2016): 62–71. http://dx.doi.org/10.1016/j.patcog.2016.04.019.

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Dissertations / Theses on the topic "Multinomial naïve bayesian"

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Van, Dyk Hendrik Oostewald. "Classification in high dimensional feature spaces / by H.O. van Dyk." Thesis, North-West University, 2009. http://hdl.handle.net/10394/4091.

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In this dissertation we developed theoretical models to analyse Gaussian and multinomial distributions. The analysis is focused on classification in high dimensional feature spaces and provides a basis for dealing with issues such as data sparsity and feature selection (for Gaussian and multinomial distributions, two frequently used models for high dimensional applications). A Naïve Bayesian philosophy is followed to deal with issues associated with the curse of dimensionality. The core treatment on Gaussian and multinomial models consists of finding analytical expressions for classification e
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Yun-DaHan and 韓昀達. "Dirichlet Priors for Markov Naïve Bayesian Classifiers with Multinomial Model for Gene Sequence Data." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/56x8f9.

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碩士<br>國立成功大學<br>資訊管理研究所<br>101<br>With the development of metagenomics and sequencing, biologists do not have to culture the microbes in a laboratory that is less than one percent of the microbes living in an ecological environment. In order to explore the diversity of species, biologists extract samples from an ecological environment directly by using the technologies for metagenomics. In the process of classifying gene sequence reads, the N-mer sliding window is generally used to extract features, and two adjacent features will have N-1 letters in common. This greatly violates the conditiona
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Hsing-ChenTsai and 蔡幸辰. "Generalized Dirichlet priors for Multinomial Naïve Bayesian Classifier on High-Dimensional Imbalanced Data Sets." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/9eqdh7.

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Mu-YingWu and 吳沐穎. "Generalized Dirichlet Priors for Naïve Bayesian Classifiers with Multinomial Models in Classifying Gene Sequence Data." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/02081199586253873510.

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碩士<br>國立成功大學<br>資訊管理研究所<br>100<br>With the passing of time, biologists are no longer limited to make observations on Petri dishes in labs. Nowadays, they can easily obtain samples from the natural world by using the new technology developed for metagenomics. Although the new technology is helpful in studying the relationships among species and the places where they live, samples obtained in this way cannot be analyzed by traditional methods. This research attempts to propose a new operational mechanism for naïve Bayesian classifiers to classify gene sequence data for biologists. Since the numb
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Chao-RuiLiu and 劉超瑞. "Methods for Setting Parameters of Generalized Dirichlet Priors for Multinomial Naïve Bayesian Classifiers in Document Classification." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/xwnqjk.

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碩士<br>國立成功大學<br>資訊管理研究所<br>101<br>Naïve Bayesian classifiers are a popular tool for classifying documents because of its computational efficiency and easy implementation. Generalized Dirichlet priors have been shown to be an effective way for improving the performance of the naïve Bayesian classifier with multinomial models, called multinomial naïve Bayesian classifiers, in document classification. For the sake of computational efficiency, the distinct words in a document set will be divided into groups, and the parameters of a generalized Dirichlet priors are determined group by group. The
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Kuan-LiangLiu and 劉冠良. "Naive Bayesian classifiers with multinomial models and noninformative generalized dirichlet priors for rRNA taxonomy assignment." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/45663880358954986554.

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博士<br>國立成功大學<br>資訊管理研究所<br>103<br>The introduction of next generation sequencing (NGS) has created a major revolution in biological ecology. Direct sequencing of hypervariable regions from rRNA genes can provide rapid and inexpensive analysis for ecological communities. In order to get deep understanding from these data, the Ribosomal Database Project developed the ‘RDP Classifier’ utilizing 8-mer nucleotide frequencies with Bayesian theorem to obtain taxonomy affiliation. This classifier is computationally efficient and works well with massive short sequences. However, the binary model employ
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Xiu-HongLin and 林修弘. "Applying genetic code for feature extraction in classifying gene sequence data by multinomial naive Bayesian classifiers." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/43705601955390568962.

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碩士<br>國立成功大學<br>資訊管理研究所<br>104<br>We often collect microorganism samples from the environment and cultivate them in laboratories, while most of them cannot be cultivated well. Collecting gene sequences from their cells is therefore a better way to study the environment microbial populations. Multinomial naïve Bayesian classifiers are often used for analyzing gene sequence data because of its computational efficiency and easy implementation. The dimension is high, and the number of class values is large in a gene sequence set. Many studies have proposed approaches to improve the accuracy of th
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Book chapters on the topic "Multinomial naïve bayesian"

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Xu, Shuo, Yan Li, and Zheng Wang. "Bayesian Multinomial Naïve Bayes Classifier to Text Classification." In Lecture Notes in Electrical Engineering. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5041-1_57.

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Hai, Nguyen Dai, and Nguyen Linh Giang. "Anomaly Detection with Multinomial Logistic Regression and Naïve Bayesian." In Lecture Notes in Electrical Engineering. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6738-6_139.

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Kapoor, Bhushan, and Sinjini Mitra. "Use of “Odds” in Bayesian Classifiers." In Encyclopedia of Data Science and Machine Learning. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-9220-5.ch162.

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Odds, log odds, and odds ratio concepts can be effectively applied in several machine learning algorithms and model evaluations. The use of these concepts has potential to make the algorithms simple, easy to interpret, and computationally more efficient. However, their implementation among the machine learning professional community has been concentrated mainly in the context of logistic regression. In this article, the authors discuss how odds, odds ratio, and log odds can be used in Bayes' theorem and multinomial naïve Bayes' classifiers. The authors will reformulate Bayes' theorem and multi
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Bhardwaj, Akashdeep. "Multinomial Naïve Bayesian Classifier Framework for Systematic Analysis of Smart IoT Devices." In Smart Home and Industrial IoT Devices: Critical Perspectives on Cyberthreats, Frameworks and Protocols. BENTHAM SCIENCE PUBLISHERS, 2024. http://dx.doi.org/10.2174/9789815256710124010007.

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Machine learning and artificial intelligence-based sentiment analysis are crucial for companies to automatically predict whether the customers are happy with their products. In this paper, a deep learning model is built to analyze thousands of reviews of Amazon Alexa to predict customer sentiment. The proposed model can be directly applied to any company that has an online presence to automatically detect customer sentiment from their reviews. The objective of this research work is to propose a suitable method for analyzing the users’ reviews of Amazon Echo and categorizing them into positive
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Veziroğlu, Merve, Erkan Eziroğlu, and İhsan Ömür Bucak. "PERFORMANCE COMPARISON BETWEEN NAIVE BAYES AND MACHINE LEARNING ALGORITHMS FOR NEWS CLASSIFICATION." In Bayesian Inference - Recent Trends. IntechOpen, 2024. http://dx.doi.org/10.5772/intechopen.1002778.

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The surge in digital content has fueled the need for automated text classification methods, particularly in news categorization using natural language processing (NLP). This work introduces a Python-based news classification system, focusing on Naive Bayes algorithms for sorting news headlines into predefined categories. Naive Bayes is favored for its simplicity and effectiveness in text classification. Our objective includes exploring the creation of a news classification system and evaluating various Naive Bayes algorithms. The dataset comprises BBC News headlines spanning technology, busine
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Conference papers on the topic "Multinomial naïve bayesian"

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Di Sipio, Claudio, Riccardo Rubei, Davide Di Ruscio, and Phuong T. Nguyen. "A Multinomial Naïve Bayesian (MNB) Network to Automatically Recommend Topics for GitHub Repositories." In EASE '20: Evaluation and Assessment in Software Engineering. ACM, 2020. http://dx.doi.org/10.1145/3383219.3383227.

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