Academic literature on the topic 'Online Bayes point machine'
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Journal articles on the topic "Online Bayes point machine"
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Aljwari, Fatima, Wahaj Alkaberi, Areej Alshutayri, Eman Aldhahri, Nahla Aljojo, and Omar Abouola. "Multi-scale Machine Learning Prediction of the Spread of Arabic Online Fake News." Postmodern Openings 13, no. 1 Sup1 (March 14, 2022): 01–14. http://dx.doi.org/10.18662/po/13.1sup1/411.
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There are a lot of research studies that look at "fake news" from an Arabic online source, but they don't look at what makes those fake news spread. The threat grows, and at some point, it gets out of hand. That's why this paper is trying to figure out how to predict the features that make Arabic online fake news spread. It's using Naive Bayes, Logistic Regression, and Random forest of Machine Learning to do this. Online news stories that were made up were used. They are found by using Term Frequency-Inverse Document Frequency (TF-IDF). The best partition for testing and validating the prediction was chosen at random and used in the analysis. So, all three machine learning classifications for predicting fake news in Arabic online were done. The results of the experiment show that Random Forest Classifier outperformed the other two algorithms. It had the best TF-IDF with an accuracy of 86 percent. Naive Bayes had an accuracy rate of 84%, and Logistic Regression had an accuracy rate of 85%, so they all did well. As such, the model shows that the features in TF-IDF are the most essential point about the content of an online Arabic fake news.
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Chairani, Chairani, Widyawan Widyawan, and Sri Suning Kusumawardani. "Machine Learning Untuk Estimasi Posisi Objek Berbasis RSS Fingerprint Menggunakan IEEE 802.11g Pada Lantai 3 Gedung JTETI UGM." JURNAL INFOTEL - Informatika Telekomunikasi Elektronika 7, no. 1 (May 10, 2015): 1. http://dx.doi.org/10.20895/infotel.v7i1.23.
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Penelitian ini membahas tentang estimasi posisi (localization) objek dalam gedung menggunakan jaringan wireless atau IEEE 802.11g dengan pendekatan Machine Learning. Metode pada pengukuran RSS menggunakan RSS-based fingerprint. Algoritma Machine Learning yang digunakan dalam memperkirakan lokasi dari pengukuran RSS-based menggunakan Naive Bayes. Localization dilakukan pada lantai 3 gedung Jurusan Teknik Elektro dan Teknologi Informasi (JTETI) dengan luas 1969,68 m2 dan memiliki 5 buah titik penempatan access point (AP). Untuk membentuk peta fingerprint digunakan dimensi 1 m x 1 m sehingga terbentuk grid sebanyak 1893 buah. Dengan menggunakan software Net Surveyor terkumpul data kekuatan sinyal yang diterima (RSS) dari jaringan wireless ke perangkat penerima (laptop) sebanyak 86.980 record. Hasil nilai rata-rata error jarak estimasi untuk localization seluruh ruangan di lantai 3 dengan menggunakan algoritma Naive Bayes pada fase offline tahap learning adalah 6,29 meter. Untuk fase online dan tahap post learning diperoleh rata-rata error jarak estimasi sebesar 7,82 meter.
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Gupta, Kanika, and Vaishnavi Mall. "COMPARATIVE ANALYSIS OF CLASSIFICATION TECHNIQUES FOR CREDIT CARD FRAUD DETECTION." International Research Journal of Computer Science 9, no. 2 (March 4, 2022): 9–15. http://dx.doi.org/10.26562/irjcs.2022.v0902.003.
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Nowadays, in the global computing environment, online payments are a necessary evil as it makes payment conveniently easier and can be done via an ample of available options like a Credit card, Debit Card, Net Banking, PayPal, Paytm available to make payments easier. The most common mode of payment used in online shopping is Credit Card as it is easier for the customers to directly transfer money from one account to another; without the withdrawal of cash at any point. However, this easy payment mode has opened up paths for multiple frauds which involve theft or illegal tampering of data of the credit card owner. Thus, with the increasing number of fraud cases and losses, it is important to find the best solution to detect credit card fraud as well as minimize the number of frauds in online systems. With the analysis of different sets of research performed on the given problem statement, we have concluded that the issue requires a substantial amount of predictions and application of machine learning to find the accuracy score of those commonly used algorithms to predict which of these three state-of-art-algorithms - Naive Bayes, Logistic Regression and K Neighbours, is best suitable to carry out the research in this area. In order to support our findings, we apply two different approaches i.e. with sampling and without sampling on these algorithms against the same dataset. We claim on the basis of our results that K Neighbours outperformed all in both the approaches and is more suitable to carry forward the fraud detection research using machine learning. The analysis will be useful for those working to derive anti-fraud strategies to predict the fraud patterns and reduce the risk during hefty transactions.
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Liu, Guoman, Yufeng Luo, and Jing Sheng. "Research on Application of Naive Bayes Algorithm Based on Attribute Correlation to Unmanned Driving Ethical Dilemma." Mathematical Problems in Engineering 2022 (August 1, 2022): 1–9. http://dx.doi.org/10.1155/2022/4163419.
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At present, unmanned driving technology has made great progress, while those research on its related ethical issues, laws, and traffic regulations are relatively lagging. In particular, it is still a problem how unmanned vehicles make a decision when they encounter ethical dilemmas where traffic collision is inevitable. So it must hinder the application and development of unmanned driving technology. Firstly, 1048575 survey data collected by Moral Machine online experiment platform is analyzed to calculate the prior probability that the straight being protector or sacrificer in ethical dilemmas with single feature. Then, 116 multifeature ethical dilemmas are designed and surveyed. The collected survey data are analyzed to determine decision-making for these ethical dilemmas by adopting the majority principle and to calculate correlation coefficient between attributes, then an improved Naive Bayes algorithm based on attribute correlation (ACNB) is established to solve the problem of unmanned driving decision in multifeature ethical dilemmas. Furthermore, these ethical dilemmas are used to test and verify traditional NB, ADOE, WADOE, CFWNB, and ACNB, respectively. According to the posterior probability that the straight being protector or sacrificer in those ethical dilemmas, classification and decision are made in these ethical dilemmas. Then, the decisions based on these algorithms are compared with human decisions to judge whether these decisions are right. The test results show that ACNB and CFWNB are more consistent with human decisions than other algorithms, and ACNB is more conductive to improve unmanned vehicle’s decision robustness than NB. Therefore, applying ACNB to unmanned vehicles has a good role, which will provide a new research point for unmanned driving ethical decision and a few references for formulating and updating traffic laws and regulations related to unmanned driving technology for traffic regulation authorities.
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Wieczorkowski, Jędrzej, and Aleksandra Suwińska. "Mowa nienawiści w mediach społecznościowych – możliwości automatycznej detekcji i eliminacji." Zarządzanie Mediami 9, no. 4 (December 31, 2021): 681–93. http://dx.doi.org/10.4467/23540214zm.21.037.14580.
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Hate Speech on Social Media – The Possibility of Automatic Detection and Elimination The article deals with the issues of hate speech and other forms of verbal aggression on the Internet as well as the possibility of their automatic detection. The paper discusses the studies confirming the partial effectiveness of text mining methods in the automatic detection of hate speech on social media. Hate speech is related to verbal aggression resulting from belonging to a group (national, racial, religious, etc.) and has become a significant problem in the social and economic context. Automatic detection significantly support the management of online news websites and social media due to the moderation of the received content. Moreover, eliminating online hate speech reduces its negative social and economic effects. The linguistic and cultural specificity of the hate speech are the problem, and the gap so far is solving the problem in Polish conditions. The study used the Tweeter database. Then, methods such as artificial neural networks, naïve Bayes classifier and support vector machine were used. The obtained results confirm the thesis about the possibility of using text mining methods in the process of reducing hate speech, but at the moment the described methods do not allow for full automation of the elimination of such content. The issue was presented in the article primarily in the context of the significance and scale of the problem and the possibility of solving it, and less from the point of view of the technical details.
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Poernomo, Abimanyu Dharma, and Suharjito Suharjito. "Indonesian online travel agent sentiment analysis using machine learning methods." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 1 (April 1, 2019): 113. http://dx.doi.org/10.11591/ijeecs.v14.i1.pp113-117.
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Many companies use social media to support their business activities. Three leading online travel agent such as Traveloka, Tiket.com, and Agoda use Facebook for supporting their business as customer service tool. This study is to measure customer satisfaction of Traveloka, Tiket.com, and Agoda by analyzing Facebook posts and comments data from their fan pages. That data will be analyzed with three machine learning algorithms such as K-Nearest Neighbors (KNN), Naïve Bayes, and Support Vector Machine (SVM) to determine the sentiment. From the classification results, data will be selected with the highest f-score to be used to calculate the Net Sentiment Score used to measure customer satisfaction. The result shows that KNN result better than Naive Bayes and SVM based on f-score. Based on Net Sentiment Score shows companies that get the highest satisfaction value of Traveloka followed by Tiket.com and Agoda
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Kabeer, Ms Shama. "Cyberbullying Detection System Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 2059–63. http://dx.doi.org/10.22214/ijraset.2021.38264.
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Abstract: Cyberbullying is an online form of harassment. By posting, commenting, sending, or distributing personal, derogatory, false, or nasty stuff about others that can shame or humiliate them, this conduct is done with the goal of harming others. Once such content is published on the internet, it remains accessible indefinitely. This activity is considered unlawful, and it is more widespread among children and teenagers. Cyberbullying is an online epidemic that has the potential to result in devastating outcomes such as violence and suicide, and so must be dealt with swiftly and properly. To detect bullying behavior in textual messages, a real-time cyberbullying detection system based on machine learning—Naïve Bayes Algorithm is presented. The model was created to determine whether a tweet was bullying or non-bullying in nature. Also, to assist victims in dealing with bullying difficulties without their identities being revealed. Keywords: Machine Learning, Cyberbullying, Naïve Bayes, Cybercrimes, Cyberbullying Detection
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Singh, Ankita. "Flexible Machine Learning based Cyberattack Detection using Spatiotemporal Patterns for Distribution Systems." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 15, 2021): 1129–34. http://dx.doi.org/10.22214/ijraset.2021.35232.
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The Article presents a versatile machine learning detection technique which is employed in distribution systems for cyberattacks considering spatiotemporal patterns. Spatiotemporal patterns are identified by the graph Laplacian which are supported on system-wide measurements. A versatile Bayes classifier is employed to coach spatiotemporal patterns which may well be compromised when cyberattacks happen. Cyberattacks are spotted by utilizing flexible Bayes classifier online.
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Pandey, Shalini, Sankeerthi Prabhakaran, N. V. Subba Reddy, and Dinesh Acharya. "Fake News Detection from Online media using Machine learning Classifiers." Journal of Physics: Conference Series 2161, no. 1 (January 1, 2022): 012027. http://dx.doi.org/10.1088/1742-6596/2161/1/012027.
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Abstract With the advancement in technology, the consumption of news has shifted from Print media to social media. The convenience and accessibility are major factors that have contributed to this shift in consumption of the news. However, this change has bought upon a new challenge in the form of “Fake news” being spread with not much supervision available on the net. In this paper, this challenge has been addressed through a Machine learning concept. The algorithms such as K-Nearest Neighbor, Support Vector Machine, Decision Tree, Naïve Bayes and Logistic regression Classifiers to identify the fake news from real ones in a given dataset and also have increased the efficiency of these algorithms by pre-processing the data to handle the imbalanced data more appropriately. Additionally, comparison of the working of these classifiers is presented along with the results. The model proposed has achieved an accuracy of 89.98% for KNN, 90.46% for Logistic Regression, 86.89% for Naïve Bayes, 73.33% for Decision Tree and 89.33% for SVM in our experiment.
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Rao Jetti, Chandrasekhar, Rehamatulla Shaik, and Sadhik Shaik. "Disease Prediction using Naïve Bayes - Machine Learning Algorithm." International Journal of Science and Healthcare Research 6, no. 4 (October 8, 2021): 17–22. http://dx.doi.org/10.52403/ijshr.20211004.
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It can occur on many occasions that you or a loved one requires urgent medical assistance, but they are unavailable due to unforeseen circumstances, or that we are unable to locate the appropriate doctor for the care. As a result, we will try to incorporate an online intelligent Smart Healthcare System in this project to solve this issue. It's a web-based programmed that allows patients to get immediate advice about their health problems. The aim of the smart healthcare system is to create a web application that can take a user's symptoms and predict diseases, as well as serve as an online consultant for various diseases. We created an expert system called Smart Health Care System, which is used to make doctors' jobs easier. A machine examines a patient at a basic level and recommends diseases that may be present. It begins by inquiring about the patient's symptoms; if the device is able to determine the relevant condition, it then recommends a doctor in the patient's immediate vicinity. The system will show the result based on the available accumulated data. We're going to use some clever data mining techniques here. We use several intelligent data mining techniques to guess the most accurate illness that could be associated with a patient's symptoms, and we use an algorithm (Naive Bayes) to map the symptoms with potential diseases based on a database of many patients' medical records. This system not only makes doctors' jobs easier, but it also benefits patients by getting them the care they need as soon as possible. Keywords: Disease Prediction, Naïve Bayes, Machine Learning Algorithm, Smart Healthcare System.
Dissertations / Theses on the topic "Online Bayes point machine"
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Harrington, Edward, and edwardharrington@homemail com au. "Aspects of Online Learning." The Australian National University. Research School of Information Sciences and Engineering, 2004. http://thesis.anu.edu.au./public/adt-ANU20060328.160810.
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Online learning algorithms have several key advantages compared to their batch learning algorithm counterparts: they are generally more memory efficient, and computationally mor efficient; they are simpler to implement; and they are able to adapt to changes where the learning model is time varying. Online algorithms because of their simplicity are very appealing to practitioners. his thesis investigates several online learning algorithms and their application. The thesis has an underlying theme of the idea of combining several simple algorithms to give better performance. In this thesis we investigate: combining weights, combining hypothesis, and (sort of) hierarchical combining.¶
Firstly, we propose a new online variant of the Bayes point machine (BPM), called the online Bayes point machine (OBPM). We study the theoretical and empirical performance of the OBPm algorithm. We show that the empirical performance of the OBPM algorithm is comparable with other large margin classifier methods such as the approximately large margin algorithm (ALMA) and methods which maximise the margin explicitly, like the support vector machine (SVM). The OBPM algorithm when used with a parallel architecture offers potential computational savings compared to ALMA. We compare the test error performance of the OBPM algorithm with other online algorithms: the Perceptron, the voted-Perceptron, and Bagging. We demonstrate that the combinationof the voted-Perceptron algorithm and the OBPM algorithm, called voted-OBPM algorithm has better test error performance than the voted-Perceptron and Bagging algorithms. We investigate the use of various online voting methods against the problem of ranking, and the problem of collaborative filtering of instances. We look at the application of online Bagging and OBPM algorithms to the telecommunications problem of channel equalization. We show that both online methods were successful at reducing the effect on the test error of label flipping and additive noise.¶
Secondly, we introduce a new mixture of experts algorithm, the fixed-share hierarchy (FSH) algorithm. The FSH algorithm is able to track the mixture of experts when the switching rate between the best experts may not be constant. We study the theoretical aspects of the FSH and the practical application of it to adaptive equalization. Using simulations we show that the FSH algorithm is able to track the best expert, or mixture of experts, in both the case where the switching rate is constant and the case where the switching rate is time varying.
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Cherief-Abdellatif, Badr-Eddine. "Contributions to the theoretical study of variational inference and robustness." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAG001.
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Cette thèse de doctorat traite de l'inférence variationnelle et de la robustesse en statistique et en machine learning. Plus précisément, elle se concentre sur les propriétés statistiques des approximations variationnelles et sur la conception d'algorithmes efficaces pour les calculer de manière séquentielle, et étudie les estimateurs basés sur le Maximum Mean Discrepancy comme règles d'apprentissage qui sont robustes à la mauvaise spécification du modèle.Ces dernières années, l'inférence variationnelle a été largement étudiée du point de vue computationnel, cependant, la littérature n'a accordé que peu d'attention à ses propriétés théoriques jusqu'à très récemment. Dans cette thèse, nous étudions la consistence des approximations variationnelles dans divers modèles statistiques et les conditions qui assurent leur consistence. En particulier, nous abordons le cas des modèles de mélange et des réseaux de neurones profonds. Nous justifions également d'un point de vue théorique l'utilisation de la stratégie de maximisation de l'ELBO, un critère numérique qui est largement utilisé dans la communauté VB pour la sélection de modèle et dont l'efficacité a déjà été confirmée en pratique. En outre, l'inférence Bayésienne offre un cadre d'apprentissage en ligne attrayant pour analyser des données séquentielles, et offre des garanties de généralisation qui restent valables même en cas de mauvaise spécification des modèles et en présence d'adversaires. Malheureusement, l'inférence Bayésienne exacte est rarement tractable en pratique et des méthodes d'approximation sont généralement employées, mais ces méthodes préservent-elles les propriétés de généralisation de l'inférence Bayésienne ? Dans cette thèse, nous montrons que c'est effectivement le cas pour certains algorithmes d'inférence variationnelle (VI). Nous proposons de nouveaux algorithmes tempérés en ligne et nous en déduisons des bornes de généralisation. Notre résultat théorique repose sur la convexité de l'objectif variationnel, mais nous soutenons que notre résultat devrait être plus général et présentons des preuves empiriques à l'appui. Notre travail donne des justifications théoriques en faveur des algorithmes en ligne qui s'appuient sur des méthodes Bayésiennes approchées.Une autre question d'intérêt majeur en statistique qui est abordée dans cette thèse est la conception d'une procédure d'estimation universelle. Cette question est d'un intérêt majeur, notamment parce qu'elle conduit à des estimateurs robustes, un thème d'actualité en statistique et en machine learning. Nous abordons le problème de l'estimation universelle en utilisant un estimateur de minimisation de distance basé sur la Maximum Mean Discrepancy. Nous montrons que l'estimateur est robuste à la fois à la dépendance et à la présence de valeurs aberrantes dans le jeu de données. Nous mettons également en évidence les liens qui peuvent exister avec les estimateurs de minimisation de distance utilisant la distance L2. Enfin, nous présentons une étude théorique de l'algorithme de descente de gradient stochastique utilisé pour calculer l'estimateur, et nous étayons nos conclusions par des simulations numériques. Nous proposons également une version Bayésienne de notre estimateur, que nous étudions à la fois d'un point de vue théorique et d'un point de vue computationnelThis PhD thesis deals with variational inference and robustness. More precisely, it focuses on the statistical properties of variational approximations and the design of efficient algorithms for computing them in an online fashion, and investigates Maximum Mean Discrepancy based estimators as learning rules that are robust to model misspecification.In recent years, variational inference has been extensively studied from the computational viewpoint, but only little attention has been put in the literature towards theoretical properties of variational approximations until very recently. In this thesis, we investigate the consistency of variational approximations in various statistical models and the conditions that ensure the consistency of variational approximations. In particular, we tackle the special case of mixture models and deep neural networks. We also justify in theory the use of the ELBO maximization strategy, a model selection criterion that is widely used in the Variational Bayes community and is known to work well in practice.Moreover, Bayesian inference provides an attractive online-learning framework to analyze sequential data, and offers generalization guarantees which hold even under model mismatch and with adversaries. Unfortunately, exact Bayesian inference is rarely feasible in practice and approximation methods are usually employed, but do such methods preserve the generalization properties of Bayesian inference? In this thesis, we show that this is indeed the case for some variational inference algorithms. We propose new online, tempered variational algorithms and derive their generalization bounds. Our theoretical result relies on the convexity of the variational objective, but we argue that our result should hold more generally and present empirical evidence in support of this. Our work presents theoretical justifications in favor of online algorithms that rely on approximate Bayesian methods. Another point that is addressed in this thesis is the design of a universal estimation procedure. This question is of major interest, in particular because it leads to robust estimators, a very hot topic in statistics and machine learning. We tackle the problem of universal estimation using a minimum distance estimator based on the Maximum Mean Discrepancy. We show that the estimator is robust to both dependence and to the presence of outliers in the dataset. We also highlight the connections that may exist with minimum distance estimators using L2-distance. Finally, we provide a theoretical study of the stochastic gradient descent algorithm used to compute the estimator, and we support our findings with numerical simulations. We also propose a Bayesian version of our estimator, that we study from both a theoretical and a computational points of view
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González, Rubio Jesús. "On the effective deployment of current machine translation technology." Doctoral thesis, Universitat Politècnica de València, 2014. http://hdl.handle.net/10251/37888.
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Machine translation is a fundamental technology that is gaining more importance
each day in our multilingual society. Companies and particulars are
turning their attention to machine translation since it dramatically cuts down
their expenses on translation and interpreting. However, the output of current
machine translation systems is still far from the quality of translations generated
by human experts. The overall goal of this thesis is to narrow down
this quality gap by developing new methodologies and tools that improve the
broader and more efficient deployment of machine translation technology.
We start by proposing a new technique to improve the quality of the
translations generated by fully-automatic machine translation systems. The
key insight of our approach is that different translation systems, implementing
different approaches and technologies, can exhibit different strengths and
limitations. Therefore, a proper combination of the outputs of such different
systems has the potential to produce translations of improved quality.
We present minimum Bayes¿ risk system combination, an automatic approach
that detects the best parts of the candidate translations and combines them
to generate a consensus translation that is optimal with respect to a particular
performance metric. We thoroughly describe the formalization of our
approach as a weighted ensemble of probability distributions and provide efficient
algorithms to obtain the optimal consensus translation according to the
widespread BLEU score. Empirical results show that the proposed approach
is indeed able to generate statistically better translations than the provided
candidates. Compared to other state-of-the-art systems combination methods,
our approach reports similar performance not requiring any additional data
but the candidate translations.
Then, we focus our attention on how to improve the utility of automatic
translations for the end-user of the system. Since automatic translations are
not perfect, a desirable feature of machine translation systems is the ability
to predict at run-time the quality of the generated translations. Quality estimation
is usually addressed as a regression problem where a quality score
is predicted from a set of features that represents the translation. However, although the concept of translation quality is intuitively clear, there is no
consensus on which are the features that actually account for it. As a consequence,
quality estimation systems for machine translation have to utilize
a large number of weak features to predict translation quality. This involves
several learning problems related to feature collinearity and ambiguity, and
due to the ¿curse¿ of dimensionality. We address these challenges by adopting
a two-step training methodology. First, a dimensionality reduction method
computes, from the original features, the reduced set of features that better
explains translation quality. Then, a prediction model is built from this
reduced set to finally predict the quality score. We study various reduction
methods previously used in the literature and propose two new ones based on
statistical multivariate analysis techniques. More specifically, the proposed dimensionality
reduction methods are based on partial least squares regression.
The results of a thorough experimentation show that the quality estimation
systems estimated following the proposed two-step methodology obtain better
prediction accuracy that systems estimated using all the original features.
Moreover, one of the proposed dimensionality reduction methods obtained the
best prediction accuracy with only a fraction of the original features. This
feature reduction ratio is important because it implies a dramatic reduction
of the operating times of the quality estimation system.
An alternative use of current machine translation systems is to embed them
within an interactive editing environment where the system and a human expert
collaborate to generate error-free translations. This interactive machine
translation approach have shown to reduce supervision effort of the user in
comparison to the conventional decoupled post-edition approach. However,
interactive machine translation considers the translation system as a passive
agent in the interaction process. In other words, the system only suggests translations
to the user, who then makes the necessary supervision decisions. As
a result, the user is bound to exhaustively supervise every suggested translation.
This passive approach ensures error-free translations but it also demands
a large amount of supervision effort from the user.
Finally, we study different techniques to improve the productivity of current
interactive machine translation systems. Specifically, we focus on the development
of alternative approaches where the system becomes an active agent
in the interaction process. We propose two different active approaches. On the
one hand, we describe an active interaction approach where the system informs
the user about the reliability of the suggested translations. The hope is that
this information may help the user to locate translation errors thus improving
the overall translation productivity. We propose different scores to measure translation reliability at the word and sentence levels and study the influence
of such information in the productivity of an interactive machine translation
system. Empirical results show that the proposed active interaction protocol
is able to achieve a large reduction in supervision effort while still generating
translations of very high quality. On the other hand, we study an active learning
framework for interactive machine translation. In this case, the system is
not only able to inform the user of which suggested translations should be
supervised, but it is also able to learn from the user-supervised translations to
improve its future suggestions. We develop a value-of-information criterion to
select which automatic translations undergo user supervision. However, given
its high computational complexity, in practice we study different selection
strategies that approximate this optimal criterion. Results of a large scale experimentation
show that the proposed active learning framework is able to
obtain better compromises between the quality of the generated translations
and the human effort required to obtain them. Moreover, in comparison to
a conventional interactive machine translation system, our proposal obtained
translations of twice the quality with the same supervision effort.González Rubio, J. (2014). On the effective deployment of current machine translation technology [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/37888
TESIS
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Koseler, Kaan Tamer. "Realization of Model-Driven Engineering for Big Data: A Baseball Analytics Use Case." Miami University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=miami1524832924255132.
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Harrington, Edward. "Aspects of Online Learning." Phd thesis, 2004. http://hdl.handle.net/1885/47147.
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Online learning algorithms have several key advantages compared to their batch learning algorithm counterparts. This thesis investigates several online learning algorithms and their application. The thesis has an underlying theme of the idea of combining several simple algorithms to give better performance. In this thesis we investigate: combining weights, combining hypothesis, and (sort of) hierarchical combining.¶ ...
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Roy, Bhupendra. "Identifying Deception in Online Reviews: Application of Machine Learning, Deep Learning and Natural Language Processing." Master's thesis, 2020. http://hdl.handle.net/10362/101187.
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Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsCustomers increasingly rate, review and research products online, (Jansen 2010). Consequently, websites containing consumer reviews are becoming targets of opinion spam. Now-a-days, people are paid money to write fake positive review online, to misguide customer and to augment sales revenue. Alternatively, people are also paid to pose as customers and to post negative fake reviews with the objective to slash competitors. These have caused menace in social media and often resulting in customer being baffled. In this study, we have explored multiple aspects of deception classification. We have explored four kinds of treatments to input i.e., the reviews using Natural Language Processing – lemmatization, stemming, POS tagging and a mix of lemmatization and POS Tagging. Also, we have explored how each of these inputs responds to different machine learning models – Logistic Regression, Naïve Bayes, Support Vector Machine, Random Forest, Extreme Gradient Boosting and Deep Learning Neural Network. We have utilized the gold standard hotel reviews dataset created by (Ott, Choi, et al. 2011) & (Ott, Cardie and Hancock, Negative Deceptive Opinion Spam 2013). Also, we used restaurant reviews dataset and doctors’ reviews dataset used by (Li, et al. 2014). We explored the usability of these models in similar domain as well as across different domains. We trained our model with 75% of hotel reviews dataset and check the accuracy of classification on similar dataset like 25% of unseen hotel reviews and on different domain dataset like unseen restaurant reviews and unseen doctors’ reviews. We perform this to create a robust model which can be applied on same domain and across different domains. Best accuracy for testing dataset of hotels achieved by us was at 91% using Deep Learning Neural Network. Logistic regression, support vector machine and random forest had similar results like neural network. Naïve Bayes also had similar accuracy; however, it had more volatility in cross domain accuracy performance. Accuracy of extreme gradient boosting was weakest among all the models that we explored. Our results are comparable and at times exceeding performance of other researchers’ work. Additionally, we have explored various models (Logistic Regression, Naïve Bayes, Support Vector Machine, Random Forest, Extreme gradient boosting, Neural network) vis a vis various input transformation method using Natural Language Processing (lemmatized unigrams, stemmed, POS tagging and a mix of lemmatization and POS Tagging).
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Khaleghi, Azadeh. "Sur quelques problèmes non-supervisés impliquant des séries temporelles hautement dèpendantes." Phd thesis, 2013. http://tel.archives-ouvertes.fr/tel-00920184.
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Cette thèse est consacrée à l'analyse théorique de problèmes non supervisés impliquant des séries temporelles hautement dépendantes. Plus particulièrement, nous abordons les deux problèmes fondamentaux que sont le problème d'estimation des points de rupture et le partitionnement de séries temporelles. Ces problèmes sont abordés dans un cadre extrêmement général oùles données sont générées par des processus stochastiques ergodiques stationnaires. Il s'agit de l'une des hypothèses les plus faibles en statistiques, comprenant non seulement, les hypothèses de modèles et les hypothèses paramétriques habituelles dans la littérature scientifique, mais aussi des hypothèses classiques d'indépendance, de contraintes sur l'espace mémoire ou encore des hypothèses de mélange. En particulier, aucune restriction n'est faite sur la forme ou la nature des dépendances, de telles sortes que les échantillons peuvent être arbitrairement dépendants. Pour chaque problème abordé, nous proposons de nouvelles méthodes non paramétriques et nous prouvons de plus qu'elles sont, dans ce cadre, asymptotiquement consistantes. Pour l'estimation de points de rupture, la consistance asymptotique se rapporte à la capacité de l'algorithme à produire des estimations des points de rupture qui sont asymptotiquement arbitrairement proches des vrais points de rupture. D'autre part, un algorithme de partitionnement est asymptotiquement consistant si le partitionnement qu'il produit, restreint à chaque lot de séquences, coïncides, à partir d'un certain temps et de manière consistante, avec le partitionnement cible. Nous montrons que les algorithmes proposés sont implémentables efficacement, et nous accompagnons nos résultats théoriques par des évaluations expérimentales. L'analyse statistique dans le cadre stationnaire ergodique est extrêmement difficile. De manière générale, il est prouvé que les vitesses de convergence sont impossibles à obtenir. Dès lors, pour deux échantillons générés indépendamment par des processus ergodiques stationnaires, il est prouvé qu'il est impossible de distinguer le cas où les échantillons sont générés par le même processus de celui où ils sont générés par des processus différents. Ceci implique que des problèmes tels le partitionnement de séries temporelles sans la connaissance du nombre de partitions ou du nombre de points de rupture ne peut admettre de solutions consistantes. En conséquence, une tâche difficile est de découvrir les formulations du problème qui en permettent une résolution dans ce cadre général. La principale contribution de cette thèse est de démontrer (par construction) que malgré ces résultats d'impossibilités théoriques, des formulations naturelles des problèmes considérés existent et admettent des solutions consistantes dans ce cadre général. Ceci inclut la démonstration du fait que le nombre de points de rupture corrects peut être trouvé, sans recourir à des hypothèses plus fortes sur les processus stochastiques. Il en résulte que, dans cette formulation, le problème des points de rupture peut être réduit à du partitionnement de séries temporelles. Les résultats présentés dans ce travail formulent les fondations théoriques pour l'analyse des données séquentielles dans un espace d'applications bien plus large.
Book chapters on the topic "Online Bayes point machine"
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Harrington, Edward, Ralf Herbrich, Jyrki Kivinen, John Platt, and Robert C. Williamson. "Online Bayes Point Machines." In Advances in Knowledge Discovery and Data Mining, 241–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36175-8_24.
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Vogt, Karsten, and Jörn Ostermann. "Soft Margin Bayes-Point-Machine Classification via Adaptive Direction Sampling." In Image Analysis, 313–24. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59126-1_26.
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Kopeć, Wiesław, Kinga Skorupska, Anna Jaskulska, Michał Łukasik, Barbara Karpowicz, Julia Paluch, Kinga Kwiatkowska, Daniel Jabłoński, and Rafał Masłyk. "XR Hackathon Going Online: Lessons Learned from a Case Study with Goethe-Institute." In Digital Interaction and Machine Intelligence, 218–28. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11432-8_22.
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AbstractIn this article we report a case study of a Language and Culture-oriented transdisciplinary XR hackathon organized with Goethe-Institut. The hackathon was hosted as an online event in November 2020 by our University Lab in collaboration with Goethe-Institut as a follow-up to our previous co-organized event within our research group Living Lab. We have improved the formula of the event based on lessons learned from its previous edition. First, in one of the two hackathon tracks we provided the participants with a custom VR framework, to serve as a starting point for their designs to skip the repetitive early development stage. In cooperation with our partner, Goethe-Institut, we have also outlined best modern research-backed language-learning practices and methods and gathered them into actionable evaluation criteria.
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Tochev, Emil, Harald Pfifer, and Svetan Ratchev. "Indirect System Condition Monitoring Using Online Bayesian Changepoint Detection." In IFIP Advances in Information and Communication Technology, 81–92. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72632-4_6.
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AbstractThis paper presents a method for online vibration analysis and a simple test bench analogue for the solder pumping system in an industrial wave-soldering machine at a Siemens factory. A common machine fault is caused by solder build-up within the pipes of the machine. This leads to a pressure drop in the system, which is replicated in the test bench by restricting the flow of water using a gate valve. The pump’s vibrational response is recorded using an accelerometer. The captured data is passed through an online Bayesian Changepoint Detection algorithm, adapted from existing literature, to detect the point at which the change in flow rate affects the pump, and thus the PCB assembly capability of the machine. This information can be used to trigger machine maintenance operations, or to isolate the vibrational response indicative of the machine fault.
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Prathap, Boppuru Rudra, Sujatha A K, Chandragiri Bala Satish Yadav, and Mummadi Mounika. "Polarity Detection on Real-Time News Data Using Opinion Mining." In Intelligent Systems and Computer Technology. IOS Press, 2020. http://dx.doi.org/10.3233/apc200124.
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Sentimental Analysis or Opinion Mining plays a vital role in the experimentation field that determines the user’s opinions, emotions and sentiments concealing a text. News on the Internet is becoming vast, and it is drawing attention and has reached the point of adequately affecting political and social realities. The popular way of checking online content, i.e. manual knowledge-based on the facts, is practically impossible because of the enormous amount of data that has now generated online. The issue can address by using Machine Learning Algorithms and Artificial Intelligence. One of the Machine Learning techniques used in this is Naive Bayes classifier. In this paper, the polarity of the news article determined whether the given news article is a positive, negative or neutral Naive Bayes Classifier, which works well with NLP (Natural Language problems) used for many purposes. It is a family of probabilistic algorithms that used to identify a word from a given text. In this, we calculate the probability of each word in a given text. Using Bayes theorem, they are getting the probabilities based on the given conditions. Topic Modeling is analytical modelling for finding the abstract of topics from a cluster of documents. Latent Dirichlet Allocation (LDA) is a topic model is used to classify the text in a given document to a specified topic. The news article is classified as positive or negative or neutral using Naive Bayes classifier by calculating the probabilities of each word from a given news article. By using topic modelling (LDA), topics of articles are detected and record data separately. The calculation of the overall sentiment of a chosen topic from different newspapers from previously recorded data done.
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Basha, Syed Muzamil, and Dharmendra Singh Rajput. "Sentiment Analysis." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 130–52. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3870-7.ch009.
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E-commerce has become a daily activity in human life. In it, the opinion and past experience related to particular product of others is playing a prominent role in selecting the product from the online market. In this chapter, the authors consider Tweets as a point of source to express users' emotions on particular subjects. This is scored with different sentiment scoring techniques. Since the patterns used in social media are relatively short, exact matches are uncommon, and taking advantage of partial matches allows one to significantly improve the accuracy of analysis on sentiments. The authors also focus on applying artificial neural fuzzy inference system (ANFIS) to train the model for better opinion mining. The scored sentiments are then classified using machine learning algorithms like support vector machine (SVM), decision tree, and naive Bayes.
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Burdescu, Dumitru Dan, and Marian Cristian Mihaescu. "Improvement of Self-Assessment Effectiveness by Activity Monitoring and Analysis." In Monitoring and Assessment in Online Collaborative Environments, 198–217. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-786-7.ch011.
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Self-assessment is one of the crucial activities within e-learning environments that provide learners with feedback regarding their level of accumulated knowledge. From this point of view, the authors think that guidance of learners in self-assessment activity must be an important goal of e-learning environment developers. The scope of the chapter is to present a recommender software system that runs along the e-learning platform. The recommender software system improves the effectiveness of self-assessment activities. The activities performed by learners represent the input data and the machine learning algorithms are used within the business logic of the recommender software system that runs along the e-learning platform. The output of the recommender software system is represented by advice given to learners in order to improve the effectiveness of self-assessment process. The methodology for obtaining improvement of self-assessment is based on embedding knowledge management into the business logic of the e-learning platform. Naive Bayes Classifier is used as machine learning algorithm for obtaining the resources (e.g., questions, chapters, and concepts) that need to be further accessed by learners. The analysis is accomplished for disciplines that are well structured according to a concept map. The input data set for the recommender software system is represented by student activities that are monitored within Tesys e-learning platform. This platform has been designed and implemented within Multimedia Applications Development Research Center at Software Engineering Department, University of Craiova. Monitoring student activities is accomplished through various techniques like creating log files or adding records into a table from a database. The logging facilities are embedded in the business logic of the e-learning platform. The e-learning platform is based on a software development framework that uses only open source software. The software architecture of the e-learning platform is based on MVC (model-view-controller) model that ensures the independence between the model (represented by MySQL database), the controller (represented by the business logic of the platform implemented in Java) and the view (represented by WebMacro which is a 100% Java open-source template language).
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Tran, Khanh Quoc, Phap Ngoc Trinh, Khoa Nguyen-Anh Tran, An Tran-Hoai Le, Luan Van Ha, and Kiet Van Nguyen. "An Empirical Investigation of Online News Classification on an Open-Domain, Large-Scale and High-Quality Dataset in Vietnamese." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210036.
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In this paper, we build a new dataset UIT-ViON (Vietnamese Online Newspaper) collected from well-known online newspapers in Vietnamese. We collect, process, and create the dataset, then experiment with different machine learning models. In particular, we propose an open-domain, large-scale, and high-quality dataset consisting of 260,000 textual data points annotated with multiple labels for evaluating Vietnamese short text classification. In addition, we present the proposed approach using transformer-based learning (PhoBERT) for Vietnamese short text classification on the dataset, which outperforms traditional machine learning (Naive Bayes and Logistic Regression) and deep learning (Text-CNN and LSTM). As a result, the proposed approach achieves the F1-score of 80.62%. This is a positive result and a premise for developing an automatic news classification system. The study is proposed to significantly save time, costs, and human resources and make it easier for readers to find news related to their interesting topics. In future, we will propose solutions to improve the quality of the dataset and improve the performance of classification models.
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Nazeer, Ishrat, Mamoon Rashid, Sachin Kumar Gupta, and Abhishek Kumar. "Use of Novel Ensemble Machine Learning Approach for Social Media Sentiment Analysis." In Advances in Social Networking and Online Communities, 16–28. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4718-2.ch002.
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Twitter is a platform where people express their opinions and come with regular updates. At present, it has become a source for many organizations where data will be extracted and then later analyzed for sentiments. Many machine learning algorithms are available for twitter sentiment analysis which are used for automatically predicting the sentiment of tweets. However, there are challenges that hinder machine learning classifiers to achieve better results in terms of classification. In this chapter, the authors are proposing a novel feature generation technique to provide desired features for training model. Next, the novel ensemble classification system is proposed for identifying sentiment in tweets through weighted majority rule ensemble classifier, which utilizes several commonly used statistical models like naive Bayes, random forest, logistic regression, which are weighted according to their performance on historical data, where weights are chosen separately for each model.
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Bulut, Faruk. "Locally-Adaptive Naïve Bayes Framework Design via Density-Based Clustering for Large Scale Datasets." In Handbook of Research on Machine Learning Techniques for Pattern Recognition and Information Security, 278–92. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3299-7.ch016.
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In this chapter, local conditional probabilities of a query point are used in classification rather than consulting a generalized framework containing a conditional probability. In the proposed locally adaptive naïve Bayes (LANB) learning style, a certain amount of local instances, which are close the test point, construct an adaptive probability estimation. In the empirical studies of over the 53 benchmark UCI datasets, more accurate classification performance has been obtained. A total 8.2% increase in classification accuracy has been gained with LANB when compared to the conventional naïve Bayes model. The presented LANB method has outperformed according to the statistical paired t-test comparisons: 31 wins, 14 ties, and 8 losses of all UCI sets.
Conference papers on the topic "Online Bayes point machine"
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Polato, Mirko, Fabio Aiolli, Luca Bergamin, and Tommaso Carraro. "Bayes Point Rule Set Learning." In ESANN 2022 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, 2022. http://dx.doi.org/10.14428/esann/2022.es2022-108.
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Jena, Soumitri, and Bhavesh R. Bhalja. "A new numeric busbar protection scheme using Bayes point machine." In 2017 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). IEEE, 2017. http://dx.doi.org/10.1109/appeec.2017.8309013.
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Li, Jiang. "Texture classification of landsat TM imagery using Bayes point machine." In the 51st ACM Southeast Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2498328.2500060.
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Qi, Yuan, Carson Reynolds, and Rosalind W. Picard. "The Bayes Point Machine for computer-user frustration detection via pressuremouse." In the 2001 workshop. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/971478.971495.
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Sunarti, S., Irawan Dwi Wahyono, Hari Putranto, Djoko Saryono, Herri Akhmad Bukhori, and Tiksno Widyatmoko. "Optimation Parameter and Attribute Naive Bayes in Machine Learning for Performance Assessment in Online Learning." In 2021 Fourth International Conference on Vocational Education and Electrical Engineering (ICVEE). IEEE, 2021. http://dx.doi.org/10.1109/icvee54186.2021.9649661.
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Corlay, Q., V. Demyanov, D. McCarthy, and D. Arnold. "Turbidite Fan Interpretation in 3D Seismic Data by Point Cloud Segmentation Using Machine Learning." In EAGE 2020 Annual Conference & Exhibition Online. European Association of Geoscientists & Engineers, 2020. http://dx.doi.org/10.3997/2214-4609.202012020.
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Gupta, Nikhil, Hilda Faraji, Daan He, and Ghanshyam Rathi. "Robust online estimation of the vanishing point for vehicle mounted cameras." In 2011 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2011. http://dx.doi.org/10.1109/mlsp.2011.6064630.
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Naramu, Avinash, and Ashwani Kumar Chandel. "Energy-based Kinetic Energy Features for Online Dynamic Security Assessment using Bayes by Backprop Machine Learning Algorithm." In 2022 1st International Conference on Sustainable Technology for Power and Energy Systems (STPES). IEEE, 2022. http://dx.doi.org/10.1109/stpes54845.2022.10006533.
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Ma, Xiaochuan, Lifeng Lai, and Shuguang Cui. "A Deep Q-Network Based Approach for Online Bayesian Change Point Detection." In 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2021. http://dx.doi.org/10.1109/mlsp52302.2021.9596490.
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Suasnawa, I., I. Caturbawa, I. Widharma, Anak Sapteka, I. Indrayana, and I. Sunaya. "Twitter Sentiment Analysis on the Implementation of Online Learning during the Pandemic using Naive Bayes and Support Vector Machine." In International Conference on Applied Science and Technology on Engineering Science. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010945500003260.
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