Relevant bibliographies by topics / Bayesův filtr

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Bayesův filtr'

Author: Grafiati
Published: 28 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Bayesův filtr"

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Deng, G. "Adaptive empirical Bayes filter." Electronics Letters 53, no. 21 (October 2017): 1398–400. http://dx.doi.org/10.1049/el.2017.1308.

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Tsyrulnikov, Michael, and Alexander Rakitko. "A Hierarchical Bayes Ensemble Kalman Filter." Physica D: Nonlinear Phenomena 338 (January 2017): 1–16. http://dx.doi.org/10.1016/j.physd.2016.07.009.

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Luft, Lukas, Federico Boniardi, Alexander Schaefer, Daniel Buscher, and Wolfram Burgard. "On the Bayes Filter for Shared Autonomy." IEEE Robotics and Automation Letters 4, no. 4 (October 2019): 3286–93. http://dx.doi.org/10.1109/lra.2019.2926217.

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Pidmohylʹnyy, O. O., O. M. Tkachenko, O. I. Holubenko, and O. V. Drobyk. "Naive Bayes Classifier as one way to filter spam mail." Connectivity 142, no. 6 (2019): 58–60. http://dx.doi.org/10.31673/2412-9070.2019.065860.

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Sokoloski, Sacha. "Implementing a Bayes Filter in a Neural Circuit: The Case of Unknown Stimulus Dynamics." Neural Computation 29, no. 9 (September 2017): 2450–90. http://dx.doi.org/10.1162/neco_a_00991.

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In order to interact intelligently with objects in the world, animals must first transform neural population responses into estimates of the dynamic, unknown stimuli that caused them. The Bayesian solution to this problem is known as a Bayes filter, which applies Bayes' rule to combine population responses with the predictions of an internal model. The internal model of the Bayes filter is based on the true stimulus dynamics, and in this note, we present a method for training a theoretical neural circuit to approximately implement a Bayes filter when the stimulus dynamics are unknown. To do this we use the inferential properties of linear probabilistic population codes to compute Bayes' rule and train a neural network to compute approximate predictions by the method of maximum likelihood. In particular, we perform stochastic gradient descent on the negative log-likelihood of the neural network parameters with a novel approximation of the gradient. We demonstrate our methods on a finite-state, a linear, and a nonlinear filtering problem and show how the hidden layer of the neural network develops tuning curves consistent with findings in experimental neuroscience.
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Adisantoso, Julio, and Wildan Rahman. "Pengukuran Kinerja Spam Filter Menggunakan Graham's Naïve Bayes Classifier." Jurnal Ilmu Komputer dan Agri-Informatika 2, no. 1 (May 1, 2013): 1. http://dx.doi.org/10.29244/jika.2.1.1-8.

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<p>Email spam telah menjadi masalah utama bagi pengguna dan penyedia jasa Internet. Pendekatan heuristic telah dilakukan untuk menyaring spam seperti black-listing atau rule-based filtering, namun hasilnya kurang memuaskan sehingga pendekatan berbasis konten (content-based filtering) menggunakan pengklasifikasi naïve Bayes lebih banyak digunakan saat ini. Penelitian ini bertujuan membandingkan pengklasifikasi naïve Bayes multinomial yang menggunakan atribut boolean dengan versi Graham, dan juga membandingkan kinerja dari dua metode untuk data latih, yaitu train-everything (TEFT) dan train-on-error (TOE). Hasil evaluasi menunjukkan bahwa naïve Bayes multinomial memiliki kinerja lebih baik dibanding versi Graham. Di samping itu, metode data latih menggunakan TEFT dapat meningkatkan akurasi model klasifikasi dibanding metode TOE.</p><p>Kata kunci: filter spam, naïve Bayes, metode training</p>
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Ye, Liang, Ying Hong Liang, and Peng Liu. "Bayesian Spam Filter Based on Distributed Architecture." Advanced Materials Research 108-111 (May 2010): 1415–20. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.1415.

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The flood of spam promotes the development of anti-spam technology. In this paper, we bring forward the Bayesian filter technology based on the distributed architecture, which can realize the sharing of the Bayesian learning outcomes among servers within the system, so as to increase the accuracy of spam recognition. We, in the paper, discuss the sharing model of information with spam features under the distributed architecture and the spam identification process; analyze the Bayes algorithm and carry out the relevant improvements; design the Bayes Filter based on distributed architecture on the above basis and verify the effect of the filter by experiments.
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Mahler, Ronald. "Exact Closed-Form Multitarget Bayes Filters." Sensors 19, no. 12 (June 24, 2019): 2818. http://dx.doi.org/10.3390/s19122818.

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The finite-set statistics (FISST) foundational approach to multitarget tracking and information fusion has inspired work by dozens of research groups in at least 20 nations; and FISST publications have been cited tens of thousands of times. This review paper addresses a recent and cutting-edge aspect of this research: exact closed-form—and, therefore, provably Bayes-optimal—approximations of the multitarget Bayes filter. The five proposed such filters—generalized labeled multi-Bernoulli (GLMB), labeled multi-Bernoulli mixture (LMBM), and three Poisson multi-Bernoulli mixture (PMBM) filter variants—are assessed in depth. This assessment includes a theoretically rigorous, but intuitive, statistical theory of “undetected targets”, and concrete formulas for the posterior undetected-target densities for the “standard” multitarget measurement model.
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Jing, Fang Fang, and Miao Cai. "A Junk SMS Filtering Application Based on Bayes Algorithm." Applied Mechanics and Materials 513-517 (February 2014): 1197–201. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1197.

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First, this paper introduces some technology about the junk SMS filtering, analyzing the principle and model characteristics of junk SMS filtering, which are based on the Bayes Algorithm. It also gives the Simulation results and the framework process of Bayesian filter. At the same time, it proposes an improved method of the Bayesian filter, which can increase the accuracy rate. Eventually it provides the conclusion.
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Liu, Zong-xiang, Yan-ni Zou, Wei-xin Xie, and Liang-qun Li. "Multi-target Bayes filter with the target detection." Signal Processing 140 (November 2017): 69–76. http://dx.doi.org/10.1016/j.sigpro.2017.05.016.

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Dissertations / Theses on the topic "Bayesův filtr"

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Havelka, Martin. "Detekce aktuálního podlaží při jízdě výtahem." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-444988.

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This diploma thesis deals with the detection of the current floor during elevator ride. This functionality is necessary for robot to move in multi-floor building. For this task, a fusion of accelerometric data during the ride of the elevator and image data obtained from the information display inside the elevator cabin is used. The research describes the already implemented solutions, data fusion methods and image classification options. Based on this part, suitable approaches for solving the problem were proposed. First, datasets from different types of elevator cabins were obtained. An algorithm for working with data from the accelerometric sensor was developed. A convolutional neural network, which was used to classify image data from displays, was selected and trained. Subsequently, the data fusion method was implemented. The individual parts were tested and evaluated. Based on their evaluation, integration into one functional system was performed. System was successfully verified and tested. Result of detection during the ride in different elevators was 97%.
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Guňka, Jiří. "Adaptivní klient pro sociální síť Twitter." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-237052.

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The goal of this term project is create user friendly client of Twitter. They may use methods of machine learning as naive bayes classifier to mentions new interests tweets. For visualissation this tweets will be use hyperbolic trees and some others methods.
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Matula, Tomáš. "Techniky umělé inteligence pro filtraci nevyžádané pošty." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236060.

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This thesis focuses on the e-mail classification and describes the basic ways of spam filtering. The Bayesian spam classifiers and artificial immune systems are analyzed and applied in this thesis. Furthermore, existing applications and evaluation metrics are described. The aim of this thesis is to design and implement an algorithm for spam filtering. Ultimately, the results are compared with selected known methods.
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Ravet, Alexandre. "Introducing contextual awareness within the state estimation process : Bayes filters with context-dependent time-heterogeneous distributions." Thesis, Toulouse, INSA, 2015. http://www.theses.fr/2015ISAT0045/document.

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Ces travaux se focalisent sur une problématique fondamentale de la robotique autonome: l'estimation d'état. En effet, la plupart des approches actuelles permettant à un robot autonome de réaliser une tâche requièrent tout d'abord l'extraction d'une information d'état à partir de mesures capteurs bruitées. Ce vecteur d'état contient un ensemble de variables caractérisant le système à un instant t, comme la position du robot, sa vitesse, etc. En robotique comme dans de nombreux autres domaines, le filtrage bayésien est devenu la solution la plus populaire pour estimer l'état d'un système de façon robuste et à haute fréquence. Le succès du filtrage bayésien réside dans sa relative simplicité, que ce soit dans la mise en oeuvre des équations récursives de filtrage, ou encore dans la représentation simplifiée et intuitive du système au travers du modèle de Markov caché d'ordre 1. Généralement, un filtre bayésien repose sur une description minimaliste de l'état du système. Cette représentation simplifiée permet de conserver un temps d'exécution réduit, mais est également la conséquence de notre compréhension partielle du fonctionnement du système physique. Tous les aspects inconnus ou non modélisés du système sont ensuite représentés de façon globale par l'adjonction de composantes de bruit. Si ces composantes de bruit constituent une représentation simple et unifiée des aspects non modélisés du système, il reste néanmoins difficile de trouver des paramètres de bruit qui sont pertinents dans tous les contextes. En effet, à l'opposé de ce principe de modélisation, la problématique de navigation autonome pose le problème de la multiplicité d'environnements différents pour lesquels il est nécessaire de s'adapter intelligemment. Cette problématique nous amène donc à réviser la modélisation des aspects inconnus du systèmes sous forme de bruits stationnaires, et requiert l'introduction d'une information de contexte au sein du processus de filtrage. Dans ce cadre, ces travaux se focalisent spécifiquement sur l'amélioration du modèle état-observation sous-jacent au filtre bayésien afin de le doter de capacités d'adaptation vis-à-vis des perturbations contextuelles modifiant les performances capteurs. L'objectif principal est donc ici de trouver l'équilibre entre complexité du modèle et modélisation précise des phénomènes physiques représentés au travers d'une information de contexte. Nous établissons cet équilibre en modifiant le modèle état-observation afin de compenser les hypothèses simplistes de bruit stationnaire tout en continuant de bénéficier du faible temps de calcul requis par les équations récursives. Dans un premier temps, nous définissons une information de contexte basée sur un ensemble de mesures capteurs brutes, sans chercher à identifier précisément la typologie réelle de contextes de navigation. Toujours au sein du formalisme bayésien, nous exploitons des méthodes d'apprentissage statistique pour identifier une distribution d'observation non stationnaire et dépendante du contexte. cette distribution repose sur l'introduction de deux nouvelles composantes: un modèle destiné à prédire le bruit d'observation pour chaque capteur, et un modèle permettant de sélectionner un sous-ensemble de mesures à chaque itération du filtre. Nos investigations concernent également l'impact des méthodes d'apprentissage: dans le contexte historique du filtrage bayésien, le modèle état-observation est traditionnellement appris de manière générative, c'est à dire de manière à expliquer au mieux les paires état-observation contenues dans les données d'apprentissage. Cette méthode est ici remise en cause puisque, bien que fondamentalement génératif, le modèle état-observation est uniquement exploité au travers des équations de filtrage, et ses capacités génératives ne sont donc jamais utilisées[...]
Prevalent approaches for endowing robots with autonomous navigation capabilities require the estimation of a system state representation based on sensor noisy information. This system state usually depicts a set of dynamic variables such as the position, velocity and orientation required for the robot to achieve a task. In robotics, and in many other contexts, research efforts on state estimation converged towards the popular Bayes filter. The primary reason for the success of Bayes filtering is its simplicity, from the mathematical tools required by the recursive filtering equations, to the light and intuitive system representation provided by the underlying Hidden Markov Model. Recursive filtering also provides the most common and reliable method for real-time state estimation thanks to its computational efficiency. To keep low computational complexity, but also because real physical systems are not perfectly understood, and hence never faithfully represented by a model, Bayes filters usually rely on a minimum system state representation. Any unmodeled or unknown aspect of the system is then encompassed within additional noise terms. On the other hand, autonomous navigation requires robustness and adaptation capabilities regarding changing environments. This creates the need for introducing contextual awareness within the filtering process. In this thesis, we specifically focus on enhancing state estimation models for dealing with context-dependent sensor performance alterations. The issue is then to establish a practical balance between computational complexity and realistic modelling of the system through the introduction of contextual information. We investigate on achieving this balance by extending the classical Bayes filter in order to compensate for the optimistic assumptions made by modeling the system through time-homogeneous distributions, while still benefiting from the recursive filtering computational efficiency. Based on raw data provided by a set of sensors and any relevant information, we start by introducing a new context variable, while never trying to characterize a concrete context typology. Within the Bayesian framework, machine learning techniques are then used in order to automatically define a context-dependent time-heterogeneous observation distribution by introducing two additional models: a model providing observation noise predictions and a model providing observation selection rules.The investigation also concerns the impact of the training method we choose. In the context of Bayesian filtering, the model we exploit is usually trained in the generative manner. Thus, optimal parameters are those that allow the model to explain at best the data observed in the training set. On the other hand, discriminative training can implicitly help in compensating for mismodeled aspects of the system, by optimizing the model parameters with respect to the ultimate system performance, the estimate accuracy. Going deeper in the discussion, we also analyse how the training method changes the meaning of the model, and how we can properly exploit this property. Throughout the manuscript, results obtained with simulated and representative real data are presented and analysed
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Sontag, Ralph. "Hat Bayes eine Chance?" Universitätsbibliothek Chemnitz, 2004. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200400556.

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Workshop "Netz- und Service-Infrastrukturen" Hat Bayes eine Chance? Seit einigen Monaten oder Jahren werden verstärkt Bayes-Filter eingesetzt, um die Nutz-E-Mail ("`Ham"') vom unerwünschten "`Spam"' zu trennen. Diese stoßen jedoch leicht an ihre Grenzen. In einem zweiten Abschnitt wird ein Filtertest der Zeitschrift c't genauer analysiert.
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Fredborg, Johan. "Spam filter for SMS-traffic." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-94161.

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Communication through text messaging, SMS (Short Message Service), is nowadays a huge industry with billions of active users. Because of the huge userbase it has attracted many companies trying to market themselves through unsolicited messages in this medium in the same way as was previously done through email. This is such a common phenomenon that SMS spam has now become a plague in many countries. This report evaluates several established machine learning algorithms to see how well they can be applied to the problem of filtering unsolicited SMS messages. Each filter is mainly evaluated by analyzing the accuracy of the filters on stored message data. The report also discusses and compares requirements for hardware versus performance measured by how many messages that can be evaluated in a fixed amount of time. The results from the evaluation shows that a decision tree filter is the best choice of the filters evaluated. It has the highest accuracy as well as a high enough process rate of messages to be applicable. The decision tree filter which was found to be the most suitable for the task in this environment has been implemented. The accuracy in this new implementation is shown to be as high as the implementation used for the evaluation of this filter. Though the decision tree filter is shown to be the best choice of the filters evaluated it turned out the accuracy is not high enough to meet the specified requirements. It however shows promising results for further testing in this area by using improved methods on the best performing algorithms.
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Valová, Alena. "Optimální metody výměny řídkých dat v senzorové síti." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2017. http://www.nusl.cz/ntk/nusl-318682.

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This thesis is focused on object tracking by a decentralized sensor network using fusion center-based and consensus-based distributed particle filters. The model includes clutter as well as missed detections of the object. The approach uses sparsity of global likelihood function, which, by means of appropriate sparse approximation and the suitable dictionaty selection can significantly reduce communication requirements in the decentralized sensor network. The master's thesis contains a design of exchange methods of sparse data in the sensor network and a comparison of the proposed methods in terms of accuracy and energy requirements.
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Delobel, Laurent. "Agrégation d'information pour la localisation d'un robot mobile sur une carte imparfaite." Thesis, Université Clermont Auvergne‎ (2017-2020), 2018. http://www.theses.fr/2018CLFAC012/document.

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La plupart des grandes villes modernes mondiales souffrent des conséquences de la pollution et des bouchons. Une solution à ce problème serait de réglementer l'accès aux centres-villes pour les voitures personnelles en faveur d'un système de transports publics constitués de navettes autonomes propulsées par une énergie n'engendrant pas de pollution gazeuse. Celles-ci pourraient desservir les usagers à la demande, en étant déroutées en fonction des appels de ceux-ci. Ces véhicules pourraient également être utilisés afin de desservir de grands sites industriels, ou bien des sites sensibles dont l'accès, restreint, doit être contrôlé. Afin de parvenir à réaliser cet objectif, un véhicule devra être capable de se localiser dans sa zone de travail. Une bonne partie des méthodes de localisation reprises par la communauté scientifique se basent sur des méthodes de type "Simultaneous Localization and Mapping" (SLAM). Ces méthodes sont capables de construire dynamiquement une carte de l'environnement ainsi que de localiser un véhicule dans une telle carte. Bien que celles-ci aient démontré leur robustesse, dans la plupart des implémentations, le partage d'une carte commune entre plusieurs robots peut s'avérer problématique. En outre, ces méthodes n'utilisent fréquemment aucune information existant au préalable et construisent la carte de leur environnement à partir de zéro.Nous souhaitons lever ces limitations, et proposons d'utiliser des cartes de type sémantique, qui existent au-préalable, par exemple comme OpenStreetMap, comme carte de base afin de se localiser. Ce type de carte contient la position de panneaux de signalisation, de feux tricolores, de murs de bâtiments etc... De telles cartes viennent presque à-coup-sûr avec des imprécisions de position, des erreurs au niveau des éléments qu'elles contiennent, par exemple des éléments réels peuvent manquer dans les données de la carte, ou bien des éléments stockés dans celles-ci peuvent ne plus exister. Afin de gérer de telles erreurs dans les données de la carte, et de permettre à un véhicule autonome de s'y localiser, nous proposons un nouveau paradigme. Tout d'abord, afin de gérer le problème de sur-convergence classique dans les techniques de fusion de données (filtre de Kalman), ainsi que le problème de mise à l'échelle, nous proposons de gérer l'intégralité de la carte par un filtre à Intersection de Covariance Partitionnée. Nous proposons également d'effacer des éléments inexistant des données de la carte en estimant leur probabilité d'existence, calculée en se basant sur les détections de ceux-ci par les capteurs du véhicule, et supprimant ceux doté d'une probabilité trop faible. Enfin, nous proposons de scanner périodiquement la totalité des données capteur pour y chercher de nouveaux amers potentiels que la carte n'intègre pas encore dans ses données, et de les y ajouter. Des expérimentations montrent la faisabilité d'un tel concept de carte dynamique de haut niveau qui serait mise à jour au-vol
Most large modern cities in the world nowadays suffer from pollution and traffic jams. A possible solution to this problem could be to regulate personnal car access into center downtown, and possibly replace public transportations by pollution-free autonomous vehicles, that could dynamically change their planned trajectory to transport people in a fully on-demand scenario. These vehicles could be used also to transport employees in a large industrial facility or in a regulated access critical infrastructure area. In order to perform such a task, a vehicle should be able to localize itself in its area of operation. Most current popular localization methods in such an environment are based on so-called "Simultaneous Localization and Maping" (SLAM) methods. They are able to dynamically construct a map of the environment, and to locate such a vehicle inside this map. Although these methods demonstrated their robustness, most of the implementations lack to use a map that would allow sharing over vehicles (map size, structure, etc...). On top of that, these methods frequently do not take into account already existing information such as an existing city map and rather construct it from scratch. In order to go beyond these limitations, we propose to use in the end semantic high-level maps, such as OpenStreetMap as a-priori map, and to allow the vehicle to localize based on such a map. They can contain the location of roads, traffic signs and traffic lights, buildings etc... Such kind of maps almost always come with some degree of imprecision (mostly in position), they also can be wrong, lacking existing but undescribed elements (landmarks), or containing in their data elements that do not exist anymore. In order to manage such imperfections in the collected data, and to allow a vehicle to localize based on such data, we propose a new strategy. Firstly, to manage the classical problem of data incest in data fusion in the presence of strong correlations, together with the map scalability problem, we propose to manage the whole map using a Split Covariance Intersection filter. We also propose to remove possibly absent landmarks still present in map data by estimating their probability of being there based on vehicle sensor detections, and to remove those with a low score. Finally, we propose to periodically scan sensor data to detect possible new landmarks that the map does not include yet, and proceed to their integration into map data. Experiments show the feasibility of such a concept of dynamic high level map that could be updated on-the-fly
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Garcia, Elmar [Verfasser], and Tino [Akademischer Betreuer] Hausotte. "Bayes-Filter zur Genauigkeitsverbesserung und Unsicherheitsermittlung von dynamischen Koordinatenmessungen / Elmar Garcia. Gutachter: Tino Hausotte." Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2014. http://d-nb.info/1054731764/34.

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Dall'ara, Jacopo. "Algoritmi per il mapping ambientale mediante array di antenne." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14267/.

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La capacità di costruire delle mappe riportanti informazioni di tipo statistico di un ambiente sconosciuto, tramite dei sensori disposti all'interno di esso, è un problema soggetto a numerose ricerche scientifiche svolte in ogni parte del mondo nelle ultime due decadi, in quanto è collegato ad innumerevoli applicazioni pratiche. Lo scopo di questo elaborato è quello di fornire una veloce introduzione teorica a tale problema, per poi proporre un metodo nuovo e più efficiente che vada a sostituire, o parzialmente completare, gli strumenti usati oggigiorno.
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Books on the topic "Bayesův filtr"

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Köhler, Bert-Uwe. Konzepte der statistischen Signalverarbeitung. Springer, 2005.

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Book chapters on the topic "Bayesův filtr"

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Hall, Mark. "A Decision Tree-Based Attribute Weighting Filter for Naive Bayes." In Research and Development in Intelligent Systems XXIII, 59–70. London: Springer London, 2007. http://dx.doi.org/10.1007/978-1-84628-663-6_5.

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Mandal, Pranab K., and V. Mandrekar. "Bayes Formula for Optimal Filter with n-ple Markov Gaussian Errors." In Recent Developments in Infinite-Dimensional Analysis and Quantum Probability, 245–52. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-010-0842-6_17.

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Fei, Huang, and Ian Reid. "Joint Bayes Filter: A Hybrid Tracker for Non-rigid Hand Motion Recognition." In Lecture Notes in Computer Science, 497–508. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24672-5_39.

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Komma, Philippe, and Andreas Zell. "Posterior Probability Estimation Techniques Embedded in a Bayes Filter for Vibration-Based Terrain Classification." In Springer Tracts in Advanced Robotics, 79–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13408-1_8.

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Nguyen, Huu-Thien-Tan, and Duy-Khanh Le. "An Approach to Improving Quality of Crawlers Using Naïve Bayes for Classifier and Hyperlink Filter." In Computational Collective Intelligence. Technologies and Applications, 525–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34630-9_54.

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Vrettos, S., and A. Stafylopatis. "Taxonomy Based Fuzzy Filtering of Search Results." In Intelligent Agents for Data Mining and Information Retrieval, 226–40. IGI Global, 2004. http://dx.doi.org/10.4018/978-1-59140-194-0.ch015.

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Our work proposes the use of topic taxonomies as part of a filtering language. Given a taxonomy, we train classifiers for every topic of it. The user is able to formulate logical rules combining the available topics, e.g., (Topic1 AND Topic2) OR Topic3, in order to filter related documents in a stream of documents. Using the classifiers, every document in the stream is assigned a belief value of belonging to the topics of the filter. These belief values are then aggregated using logical operators to yield the belief to the filter. In that framework, we are concerned with the operators that provide the best filtering performance for the user. In our study, Support Vector Machines (SVMs) and Naïve Bayes (NB) classifiers were used to provide topic probabilities. Fuzzy aggregation operators were tested on the Reuters text corpus and showed better results than their Boolean counterparts. Moreover, the application of Ordered Weighted Averaging (OWA) operators considerably improved the performance of fuzzy aggregation, especially in the case of NB classifiers. Finally, we describe a filtering system to exemplify the use of fuzzy filtering.
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Chinnaswamy, Arunkumar, and Ramakrishnan Srinivasan. "Performance Analysis of Classifiers on Filter-Based Feature Selection Approaches on Microarray Data." In Bio-Inspired Computing for Information Retrieval Applications, 41–70. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2375-8.ch002.

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The process of Feature selection in machine learning involves the reduction in the number of features (genes) and similar activities that results in an acceptable level of classification accuracy. This paper discusses the filter based feature selection methods such as Information Gain and Correlation coefficient. After the process of feature selection is performed, the selected genes are subjected to five classification problems such as Naïve Bayes, Bagging, Random Forest, J48 and Decision Stump. The same experiment is performed on the raw data as well. Experimental results show that the filter based approaches reduce the number of gene expression levels effectively and thereby has a reduced feature subset that produces higher classification accuracy compared to the same experiment performed on the raw data. Also Correlation Based Feature Selection uses very fewer genes and produces higher accuracy compared to Information Gain based Feature Selection approach.
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Tiwari, Arvind Kumar. "Introduction to Machine Learning." In Ubiquitous Machine Learning and Its Applications, 1–14. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2545-5.ch001.

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Machine learning refers to the changes in systems that perform tasks associated with artificial intelligence. This chapter presents introduction types and application of machine learning. This chapter also presents the basic concepts related to feature selection techniques such as filter, wrapper and hybrid methods and various machine learning techniques such as artificial neural network, Naive Bayes classifier, support vector machine, k-nearest-neighbor, decision trees, bagging, boosting, random subspace method, random forests, k-means clustering and deep learning. In the last the performance measure of the classifier is presented.
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Tiwari, Arvind Kumar. "Introduction to Machine Learning." In Deep Learning and Neural Networks, 41–51. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0414-7.ch003.

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Machine learning refers to the changes in systems that perform tasks associated with artificial intelligence. This chapter presents introduction types and application of machine learning. This chapter also presents the basic concepts related to feature selection techniques such as filter, wrapper and hybrid methods and various machine learning techniques such as artificial neural network, Naive Bayes classifier, support vector machine, k-nearest-neighbor, decision trees, bagging, boosting, random subspace method, random forests, k-means clustering and deep learning. In the last the performance measure of the classifier is presented.
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Hamou, Reda Mohamed, Abdelmalek Amine, and Moulay Tahar. "The Impact of the Mode of Data Representation for the Result Quality of the Detection and Filtering of Spam." In Ontologies and Big Data Considerations for Effective Intelligence, 150–68. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2058-0.ch004.

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Spam is now of phenomenal proportions since it represents a high percentage of total emails exchanged on the Internet. In the fight against spam, we are using this article to develop a hybrid algorithm based primarily on the probabilistic model in this case, Naïve Bayes, for weighting the terms of the matrix term -category and second place used an algorithm of unsupervised learning (K-means) to filter two classes, namely spam and ham (legitimate email). To determine the sensitive parameters that make up the classifications we are interested in studying the content of the messages by using a representation of messages using the n-gram words and characters independent of languages (because a message may be received in any language) to later decide what representation to use to get a good classification. We have chosen several metrics as evaluation to validate our results.
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Conference papers on the topic "Bayesův filtr"

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Kanazaki, Hirofumi, Takehisa Yairi, Kazuo Machida, Kenji Kondo, and Yoshihiko Matsukawa. "Variational Bayes Data Association Filter." In 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information. IEEE, 2007. http://dx.doi.org/10.1109/issnip.2007.4496877.

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Battistelli, Giorgio, Luigi Chisci, Lin Gao, and Daniela Selvi. "Event-triggered distributed Bayes filter." In 2019 18th European Control Conference (ECC). IEEE, 2019. http://dx.doi.org/10.23919/ecc.2019.8795966.

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Garcia, E., and T. Hausotte. "P8 - Bayes-Filter für dynamische Koordinatenmessungen." In AHMT 2014 - Symposium des Arbeitskreises der Hochschullehrer für Messtechnik. AHMT - Arbeitskreis der Hochschullehrer für Messtechnik, 2014. http://dx.doi.org/10.5162/ahmt2014/p8.

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Yan, Han-Bing, and Ya-Shu Liu. "Spam filter based on incremental Bayes arithmetic." In 2011 International Conference on Electrical and Control Engineering (ICECE). IEEE, 2011. http://dx.doi.org/10.1109/iceceng.2011.6056834.

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Liu, Zong Xiang, and Xiu Jiang Tang. "Particle Implementation of Marginal Distribution Bayes Filter." In 2018 14th IEEE International Conference on Signal Processing (ICSP). IEEE, 2018. http://dx.doi.org/10.1109/icsp.2018.8652418.

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Kelestemur, Tarik, Colin Keil, John P. Whitney, Robert Platt, and Taskin Padir. "Learning Bayes Filter Models for Tactile Localization." In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020. http://dx.doi.org/10.1109/iros45743.2020.9341420.

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Challa, Subhash, and Farhan A. Faruqi. "Passive position location using Bayes' conditional density filter." In AeroSense '97, edited by Scott A. Speigle. SPIE, 1997. http://dx.doi.org/10.1117/12.277221.

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Wang, Xiaoxu, Haoran Cui, Quan Pan, Yan Liang, Jinwen Hu, and Zhao Xu. "Linear Gaussian Regression Filter Based on Variational Bayes." In 2018 21st International Conference on Information Fusion (FUSION 2018). IEEE, 2018. http://dx.doi.org/10.23919/icif.2018.8455744.

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Mahler, Ronald P. S. "Extended first-order Bayes filter for force aggregation." In AeroSense 2002, edited by Oliver E. Drummond. SPIE, 2002. http://dx.doi.org/10.1117/12.478503.

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Okita, Nori, and H. J. Sommer. "A Novel Gait and Foot Slip Detection Algorithm for Walking Robots." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-6021.

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A novel gait and slip detection algorithm for walking robots using an inertial measurement unit was developed. An unscented Kalman filter was formulated with a simple dynamic model as a block on a slope without translations. Considerable prediction errors resulted when unmodeled dynamics (i.e., translation) occurred. These prediction errors were used in a binary Bayes filter to estimate the probability of gait and slip states. A proof of concept experiment was conducted with a monopedal walker under three floor conditions (nonslip, poly, and poly-oil) and three orientations (flat, uphill, and downhill). Realtime and offline detection at 100 Hz were successful. Continuous gait cycles were detected in proper order. Slip detection was successful except for very mild slips involving small jerk. The proposed algorithm provided a robust gait and slip detection method with a single set of parameters without knowledge of floor conditions and inclinations.
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