Journal articles: '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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Taha, Ahmed Majid, Soong-Der Chen, and Aida Mustapha. "Bat Algorithm Based Hybrid Filter-Wrapper Approach." Advances in Operations Research 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/961494.

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This paper presents a new hybrid of Bat Algorithm (BA) based on Mutual Information (MI) and Naive Bayes called BAMI. In BAMI, MI was used to identify promising features which could potentially accelerate the process of finding the best known solution. The promising features were then used to replace several of the randomly selected features during the search initialization. BAMI was tested over twelve datasets and compared against the standard Bat Algorithm guided by Naive Bayes (BANV). The results showed that BAMI outperformed BANV in all datasets in terms of computational time. The statistical test indicated that BAMI has significantly lower computational time than BANV in six out of twelve datasets, while maintaining the effectiveness. The results also showed that BAMI performance was not affected by the number of features or samples in the dataset. Finally, BAMI was able to find the best known solutions with limited number of iterations.

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Brunker, Alexander, Thomas Wohlgemuth, Michael Frey, and Frank Gauterin. "Dual-Bayes Localization Filter Extension for Safeguarding in the Case of Uncertain Direction Signals." Sensors 18, no. 10 (October 19, 2018): 3539. http://dx.doi.org/10.3390/s18103539.

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In order to run a localization filter for parking systems in real time, the directional information must be directly available when a distance measurement of the wheel speed sensor is detected. When the vehicle is launching, the wheel speed sensors may already detect distance measurement in the form of Delta-Wheel-Pulse-Counts (DWPCs) without having defined a rolling direction. This phenomenon is particularly problematic during parking maneuvers, where many small correction strokes are made. If a localization filter is used for positioning, the restrained DWPCs cannot process in real time. Without directional information in the form of a rolling direction signal, the filter has to ignore the DWPCs or artificially stop until a rolling direction signal is present. For this reason, methods for earlier estimation of the rolling direction based on the pattern of the incoming DWPCs and based on the force equilibrium have been presented. Since the new methods still have their weaknesses and a wrong estimation of the rolling direction can occur, an extension of a so-called Dual-Localization filter approach is presented. The Dual-Localization filter uses two localization filters and an intelligent initialization logic that ensures that both filters move in opposite directions at launching. The primary localization filter uses the estimated and the secondary one the opposite direction. As soon as a valid rolling direction signal is present, an initialization logic is used to decide which localization filter has previously moved in the true direction. The localization filter that has moved in the wrong direction is initialized with the states and covariances of the other localization filter. This extension allows for a fast and real-time capability to be achieved, and the accumulated velocity error can be dramatically reduced.

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Wang, Guoqing, Zhongxing Gao, Yonggang Zhang, and Bin Ma. "Adaptive Maximum Correntropy Gaussian Filter Based on Variational Bayes." Sensors 18, no. 6 (June 17, 2018): 1960. http://dx.doi.org/10.3390/s18061960.

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Jiang, Liangxiao, Lungan Zhang, Chaoqun Li, and Jia Wu. "A Correlation-Based Feature Weighting Filter for Naive Bayes." IEEE Transactions on Knowledge and Data Engineering 31, no. 2 (February 1, 2019): 201–13. http://dx.doi.org/10.1109/tkde.2018.2836440.

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Kumar, Mohit, Norbert Stoll, and Regina Stoll. "Variational Bayes for a Mixed Stochastic/Deterministic Fuzzy Filter." IEEE Transactions on Fuzzy Systems 18, no. 4 (August 2010): 787–801. http://dx.doi.org/10.1109/tfuzz.2010.2048331.

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Mandrekar, Vidyadhar, Thilo Meyer-Brandis, and Frank Proske. "A Bayes Formula for Nonlinear Filtering with Gaussian and Cox Noise." Journal of Probability and Statistics 2011 (2011): 1–15. http://dx.doi.org/10.1155/2011/259091.

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A Bayes-type formula is derived for the nonlinear filter where the observation contains both general Gaussian noise as well as Cox noise whose jump intensity depends on the signal. This formula extends the well-known Kallianpur-Striebel formula in the classical non-linear filter setting. We also discuss Zakai-type equations for both the unnormalized conditional distribution as well as unnormalized conditional density in case the signal is a Markovian jump diffusion.

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Qiang, Zheng Jie, and He Xin Zhang. "Laser Active Image De-Noising Algorithm Based on Lifting-Wavelet." Advanced Materials Research 1044-1045 (October 2014): 1194–200. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.1194.

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According to the characteristics of laser active imaging and the real-time image features needed, a new image de-noising algorithm based on the voting median filter and integer lifting-wavelet transform was proposed. Firstly, the noise image was dealt with voting median filter to eliminate the interference of impulse noise. Secondly, the noise image was decomposed with lifting-wavelet, wavelet coefficients was processed with Bayes adaptive threshold method. Finally got de-noised image by inverse transform. Through compared with the standard median filter, lifting-wavelet transform, the ordinary wavelet combined with the median filter, experimental results show that this method has advanced de-noising performance and edge retention. Meanwhile it has the less computation time.

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Ma, Xiao, Peter Karkus, David Hsu, and Wee Sun Lee. "Particle Filter Recurrent Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5101–8. http://dx.doi.org/10.1609/aaai.v34i04.5952.

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Recurrent neural networks (RNNs) have been extraordinarily successful for prediction with sequential data. To tackle highly variable and multi-modal real-world data, we introduce Particle Filter Recurrent Neural Networks (PF-RNNs), a new RNN family that explicitly models uncertainty in its internal structure: while an RNN relies on a long, deterministic latent state vector, a PF-RNN maintains a latent state distribution, approximated as a set of particles. For effective learning, we provide a fully differentiable particle filter algorithm that updates the PF-RNN latent state distribution according to the Bayes rule. Experiments demonstrate that the proposed PF-RNNs outperform the corresponding standard gated RNNs on a synthetic robot localization dataset and 10 real-world sequence prediction datasets for text classification, stock price prediction, etc.

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Park, Chee-Hyun, and Joon-Hyuk Chang. "Robust Shrinkage Range Estimation Algorithms Based on Hampel and Skipped Filters." Wireless Communications and Mobile Computing 2019 (January 1, 2019): 1–9. http://dx.doi.org/10.1155/2019/6592406.

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Herein, we present robust shrinkage range estimation algorithms for which received signal strength measurements are used to estimate the distance between emitter and sensor. The concepts of robustness for the Hampel filter and skipped filter are combined with shrinkage for the positive blind minimax and Bayes shrinkage estimation. It is demonstrated that the estimation accuracies of the proposed methods are higher than those of the existing median-based shrinkage methods through extensive simulations.

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Hendra, Asep, and Fitriyani Fitriyani. "Analisis Sentimen Review Halodoc Menggunakan Nai ̈ve Bayes Classifier." JISKA (Jurnal Informatika Sunan Kalijaga) 6, no. 2 (May 3, 2021): 78–89. http://dx.doi.org/10.14421/jiska.2021.6.2.78-89.

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Healthcare service has the role to help and serve people to access medical services, i.e. providing medicines, medical consultation, or health control. Healthcare service has been transforming to a digital platform. Halodoc is one of the digital platforms that people can use for free or paid, user can also give reviews of Halodoc’s performance and services on Google Play Store to give feedback that Halodoc can use to evaluate and improve the app. The Google Play Store review is increasing every day. Therefore an analysis for the review with sentiment analysis for Halodoc’s review is needed, first phase of sentiment analysis for the review is preprocessing which has tokenization, transform to lower cases, filter stopword, dan filter token (by length) processes. The data is divided into two positive and negative classes with cross-validation and a k-fold validation value of 10, using Naïve Bayes Classifier algorithm with 81,68% accuracy and AUC 0.756, categorized as fair classification.

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Hall, Mark. "A decision tree-based attribute weighting filter for naive Bayes." Knowledge-Based Systems 20, no. 2 (March 2007): 120–26. http://dx.doi.org/10.1016/j.knosys.2006.11.008.

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Liu, Zong-Xiang, Mian Wu, and Jie Gan. "Marginal Distribution Multi-Target Bayes Filter With Assignment of Measurements." IEEE Access 8 (2020): 118235–44. http://dx.doi.org/10.1109/access.2020.3004522.

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Vernanda, Yustinus, Seng Hansun, and Marcel Bonar Kristanda. "Indonesian language email spam detection using N-gram and Naïve Bayes algorithm." Bulletin of Electrical Engineering and Informatics 9, no. 5 (October 1, 2020): 2012–19. http://dx.doi.org/10.11591/eei.v9i5.2444.

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Indonesia is ranked the top 8th out of the total country population in the world for the global spammers. Web-based spam filter service with the REST API type can be used to detect email spam in the Indonesian language on the email server or various types of email server applications. With REST API, then there will be data exchange between the applications with JSON data type using existing HTTP commands. One type of spam filter commonly used is Bayesian Filtering, where the Naïve Bayes algorithm is used as a classification algorithm. Meanwhile, the N-gram method is used to increase the accuracy of the implementation of the Naïve Bayes algorithm in this study. N-gram and Naïve Bayes algorithms to detect spam email in the Indonesian language have successfully been implemented with accuracy around 0.615 until 0.94, precision at 0.566 until 0.924, recall at 0.96 until 1.00, and F-measure at 0.721 until 0.942. The best solution is found by using the 5-gram method with the highest score of accuracy at 0.94, precision at 0.924, recall at 0.96, and F-measure value at 0.942.

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Sondergaard, Thomas, and Pierre F. J. Lermusiaux. "Data Assimilation with Gaussian Mixture Models Using the Dynamically Orthogonal Field Equations. Part I: Theory and Scheme." Monthly Weather Review 141, no. 6 (June 1, 2013): 1737–60. http://dx.doi.org/10.1175/mwr-d-11-00295.1.

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Abstract This work introduces and derives an efficient, data-driven assimilation scheme, focused on a time-dependent stochastic subspace that respects nonlinear dynamics and captures non-Gaussian statistics as it occurs. The motivation is to obtain a filter that is applicable to realistic geophysical applications, but that also rigorously utilizes the governing dynamical equations with information theory and learning theory for efficient Bayesian data assimilation. Building on the foundations of classical filters, the underlying theory and algorithmic implementation of the new filter are developed and derived. The stochastic Dynamically Orthogonal (DO) field equations and their adaptive stochastic subspace are employed to predict prior probabilities for the full dynamical state, effectively approximating the Fokker–Planck equation. At assimilation times, the DO realizations are fit to semiparametric Gaussian Mixture Models (GMMs) using the Expectation-Maximization algorithm and the Bayesian Information Criterion. Bayes’s law is then efficiently carried out analytically within the evolving stochastic subspace. The resulting GMM-DO filter is illustrated in a very simple example. Variations of the GMM-DO filter are also provided along with comparisons with related schemes.

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Metref, S., E. Cosme, C. Snyder, and P. Brasseur. "A non-Gaussian analysis scheme using rank histograms for ensemble data assimilation." Nonlinear Processes in Geophysics 21, no. 4 (August 25, 2014): 869–85. http://dx.doi.org/10.5194/npg-21-869-2014.

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Abstract. One challenge of geophysical data assimilation is to address the issue of non-Gaussianities in the distributions of the physical variables ensuing, in many cases, from nonlinear dynamical models. Non-Gaussian ensemble analysis methods fall into two categories, those remapping the ensemble particles by approximating the best linear unbiased estimate, for example, the ensemble Kalman filter (EnKF), and those resampling the particles by directly applying Bayes' rule, like particle filters. In this article, it is suggested that the most common remapping methods can only handle weakly non-Gaussian distributions, while the others suffer from sampling issues. In between those two categories, a new remapping method directly applying Bayes' rule, the multivariate rank histogram filter (MRHF), is introduced as an extension of the rank histogram filter (RHF) first introduced by Anderson (2010). Its performance is evaluated and compared with several data assimilation methods, on different levels of non-Gaussianity with the Lorenz 63 model. The method's behavior is then illustrated on a simple density estimation problem using ensemble simulations from a coupled physical–biogeochemical model of the North Atlantic ocean. The MRHF performs well with low-dimensional systems in strongly non-Gaussian regimes.

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Shen, Chien-Lung, Tzu-Hao Huang, Po-Chun Hsu, Ya-Chi Ko, Fen-Ling Chen, Wei-Chun Wang, Tsair Kao, and Chia-Tai Chan. "Respiratory Rate Estimation by Using ECG, Impedance, and Motion Sensing in Smart Clothing." Journal of Medical and Biological Engineering 37, no. 6 (July 1, 2017): 826–42. http://dx.doi.org/10.1007/s40846-017-0247-z.

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Abstract The needs for light-weight and soft smart clothing in homecare have been rising since the past decade. Many smart textile sensors have been developed and applied to automatic physiological and user-centered environmental status recognition. In the present study, we propose wearable multi-sensor smart clothing for homecare monitoring based on an economic fabric electrode with high elasticity and low resistance. The wearable smart clothing integrated with heterogeneous sensors is capable to measure multiple human biosignals (ECG and respiration), acceleration, and gyro information. Five independent respiratory signals (electric impedance plethysmography, respiratory induced frequency variation, respiratory induced amplitude variation, respiratory induced intensity variation, and respiratory induced movement variation) are obtained. The smart clothing can provide accurate respiratory rate estimation by using three different techniques (Naïve Bayes inference, static Kalman filter, and dynamic Kalman filter). During the static sitting experiments, respiratory induced frequency variation has the best performance; whereas during the running experiments, respiratory induced amplitude variation has the best performance. The Naïve Bayes inference and dynamic Kalman filter have shown good results. The novel smart clothing is soft, elastic, and washable and it is suitable for long-term monitoring in homecare medical service and healthcare industry.

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Zhang, Heng Hao, Xiu Yun Meng, and Zao Zhen Liu. "The Amalgamated Particle Filter Algorithm Based on the Bayes Theory and Used to Control Navigation’s System Interferers." Advanced Materials Research 457-458 (January 2012): 1508–13. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.1508.

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It introduces the algorithm of particle filter and the Bayes theory. The way is improved necessarily in the navigation system and then used in it. When one channel in the system is interfered and can’t work, using the changing particle’s weight factors which stand for the emanative channel in the system can control the interferer. The simulation shows the particle filter algorithm can through other channels control the interferer which occurs in the one channel. So the system also can work naturally.

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Yang, Xiu Jie, and Ping Chen. "SAR Image Denoising Algorithm Based on Bayes Wavelet Shrinkage and Fast Guided Filter." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 1 (January 20, 2019): 107–13. http://dx.doi.org/10.20965/jaciii.2019.p0107.

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To remove the speckle noise of synthetic aperture radar (SAR) images, a novel denoising algorithm based on Bayes wavelet shrinkage and a fast guided filter is proposed. According to the statistical properties of SAR images, the noise-free signal and speckle noise in the wavelet domain are modeled as Laplace and Fisher-Tippett distributions respectively. Then a new wavelet shrinkage algorithm is obtained by adopting the Bayes maximum a posteriori estimation. Speckle noise in the high-frequency domain of SAR images is shrunk by this new wavelet shrinkage algorithm. As the wavelet coefficients of the low-frequency domain also contain some speckle noise, speckle noise in the low-frequency domain can be further filtered by the fast guided filter. The result of the denoising experiments of simulated SAR images and real SAR images demonstrate that the proposed algorithm has the ability to better denoise and preserve edge information.

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Balogun, Abdullateef O., Shuib Basri, Saipunidzam Mahamad, Said Jadid Abdulkadir, Luiz Fernando Capretz, Abdullahi A. Imam, Malek A. Almomani, Victor E. Adeyemo, and Ganesh Kumar. "Empirical Analysis of Rank Aggregation-Based Multi-Filter Feature Selection Methods in Software Defect Prediction." Electronics 10, no. 2 (January 15, 2021): 179. http://dx.doi.org/10.3390/electronics10020179.

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Selecting the most suitable filter method that will produce a subset of features with the best performance remains an open problem that is known as filter rank selection problem. A viable solution to this problem is to independently apply a mixture of filter methods and evaluate the results. This study proposes novel rank aggregation-based multi-filter feature selection (FS) methods to address high dimensionality and filter rank selection problem in software defect prediction (SDP). The proposed methods combine rank lists generated by individual filter methods using rank aggregation mechanisms into a single aggregated rank list. The proposed methods aim to resolve the filter selection problem by using multiple filter methods of diverse computational characteristics to produce a dis-joint and complete feature rank list superior to individual filter rank methods. The effectiveness of the proposed method was evaluated with Decision Tree (DT) and Naïve Bayes (NB) models on defect datasets from NASA repository. From the experimental results, the proposed methods had a superior impact (positive) on prediction performances of NB and DT models than other experimented FS methods. This makes the combination of filter rank methods a viable solution to filter rank selection problem and enhancement of prediction models in SDP.

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Zhang, Lungan, Liangxiao Jiang, and Chaoqun Li. "A New Feature Selection Approach to Naive Bayes Text Classifiers." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 02 (February 2016): 1650003. http://dx.doi.org/10.1142/s0218001416500038.

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Handling text data is a challenge for machine learning because text data is high dimensional in many cases. Feature selection has been approved to be an effective approach to handle high-dimensional data. Feature selection approaches can be broadly divided into two categories: filter approaches and wrapper approaches. Generally, wrapper approaches have superior accuracy compared to filters, but filters always run faster than wrapper approaches. In order to integrate the advantages of filter approaches and wrapper approaches, we propose a gain ratio-based hybrid feature selection approach to naive Bayes text classifiers. The hybrid feature selection approach uses base classifiers to evaluate feature subsets like wrapper approaches, but it need not repeatedly search feature subsets and build base classifiers. The experimental results on large suite of benchmark text datasets show that the proposed hybrid feature selection approach significantly improves the classification accuracy of the original naive Bayes text classifiers while does not incur the high time complexity that characterizes wrapper approaches.

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Qayoom, Insha, and Sameena Naaz. "Discriminant Analysis and Naïve Bayes Classifier-Based Biometric Identification Using Finger Veins." International Journal of Computer Vision and Image Processing 9, no. 4 (October 2019): 15–27. http://dx.doi.org/10.4018/ijcvip.2019100102.

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Finger vein identification is a dominating method of biometric technology used for authentication in a highly secure environment. Vein patterns are unique for each individual and it is underneath skin so there is less chance for forgery. In the current research work, finger vein features are extracted and verified for the purpose of authentication. The first step in this work is to pre-process the image obtained from the database. In order to get the region of interest (ROI) the threshold value is calculated using a standard deviation method followed by morphology-based functions available in the MATLAB software. After pre -processing a Gabor filter, fast filter, and freak descriptors are used. The features calculated at the freak descriptor processing are trained on classifiers namely discriminant and Naïve Bayes. The features trained to the classifiers are then fed again into the classifiers and cross verified to update the results of accuracy. The accuracy calculated using discriminant analysis is 94.46% and by using Naïve Bayes is 98.38%.

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Schaffrin, B., and J. H. Kwon. "A Bayes filter in Friedland form for INS/GPS vector gravimetry." Geophysical Journal International 149, no. 1 (April 2002): 64–75. http://dx.doi.org/10.1046/j.1365-246x.2002.01640.x.

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LEE, CHANG-HWAN. "AN INFORMATION-THEORETIC FILTER METHOD FOR FEATURE WEIGHTING IN NAIVE BAYES." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 05 (July 31, 2014): 1451007. http://dx.doi.org/10.1142/s0218001414510070.

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In spite of its simplicity, naive Bayesian learning has been widely used in many data mining applications. However, the unrealistic assumption that all features are equally important negatively impacts the performance of naive Bayesian learning. In this paper, we propose a new method that uses a Kullback–Leibler measure to calculate the weights of the features analyzed in naive Bayesian learning. Its performance is compared to that of other state-of-the-art methods over a number of datasets.

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Vo, Ba-Ngu, Ba-Tuong Vo, and Dinh Phung. "Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter." IEEE Transactions on Signal Processing 62, no. 24 (December 2014): 6554–67. http://dx.doi.org/10.1109/tsp.2014.2364014.

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Chen, Zhenghua, Yanbing Yang, Chaoyang Jiang, Jie Hao, and Le Zhang. "Light Sensor Based Occupancy Estimation via Bayes Filter With Neural Networks." IEEE Transactions on Industrial Electronics 67, no. 7 (July 2020): 5787–97. http://dx.doi.org/10.1109/tie.2019.2934028.

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Xu, Wenqiang, Liangxiao Jiang, and Liangjun Yu. "An attribute value frequency-based instance weighting filter for naive Bayes." Journal of Experimental & Theoretical Artificial Intelligence 31, no. 2 (November 9, 2018): 225–36. http://dx.doi.org/10.1080/0952813x.2018.1544284.

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Liu, Zong-xiang, Li-juan Li, Wei-xin Xie, and Liang-qun Li. "Two implementations of marginal distribution Bayes filter for nonlinear Gaussian models." AEU - International Journal of Electronics and Communications 69, no. 9 (September 2015): 1297–304. http://dx.doi.org/10.1016/j.aeue.2015.05.007.

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Nuss, Dominik, Stephan Reuter, Markus Thom, Ting Yuan, Gunther Krehl, Michael Maile, Axel Gern, and Klaus Dietmayer. "A random finite set approach for dynamic occupancy grid maps with real-time application." International Journal of Robotics Research 37, no. 8 (July 2018): 841–66. http://dx.doi.org/10.1177/0278364918775523.

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Grid mapping is a well-established approach for environment perception in robotic and automotive applications. Early work suggests estimating the occupancy state of each grid cell in a robot’s environment using a Bayesian filter to recursively combine new measurements with the current posterior state estimate of each grid cell. This filter is often referred to as binary Bayes filter. A basic assumption of classical occupancy grid maps is a stationary environment. Recent publications describe bottom-up approaches using particles to represent the dynamic state of a grid cell and outline prediction-update recursions in a heuristic manner. This paper defines the state of multiple grid cells as a random finite set, which allows to model the environment as a stochastic, dynamic system with multiple obstacles, observed by a stochastic measurement system. It motivates an original filter called the probability hypothesis density / multi-instance Bernoulli (PHD/MIB) filter in a top-down manner. The paper presents a real-time application serving as a fusion layer for laser and radar sensor data and describes in detail a highly efficient parallel particle filter implementation. A quantitative evaluation shows that parameters of the stochastic process model affect the filter results as theoretically expected and that appropriate process and observation models provide consistent state estimation results.

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Tödter, Julian, and Bodo Ahrens. "A Second-Order Exact Ensemble Square Root Filter for Nonlinear Data Assimilation." Monthly Weather Review 143, no. 4 (March 31, 2015): 1347–67. http://dx.doi.org/10.1175/mwr-d-14-00108.1.

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Abstract The ensemble Kalman filter (EnKF) and its deterministic variants, mostly square root filters such as the ensemble transform Kalman filter (ETKF), represent a popular alternative to variational data assimilation schemes and are applied in a wide range of operational and research activities. Their forecast step employs an ensemble integration that fully respects the nonlinear nature of the analyzed system. In the analysis step, they implicitly assume the prior state and observation errors to be Gaussian. Consequently, in nonlinear systems, the analysis mean and covariance are biased, and these filters remain suboptimal. In contrast, the fully nonlinear, non-Gaussian particle filter (PF) only relies on Bayes’s theorem, which guarantees an exact asymptotic behavior, but because of the so-called curse of dimensionality it is exposed to weight collapse. Here, it is shown how to obtain a new analysis ensemble whose mean and covariance exactly match the Bayesian estimates. This is achieved by a deterministic matrix square root transformation of the forecast ensemble, and subsequently a suitable random rotation that significantly contributes to filter stability while preserving the required second-order statistics. The properties and performance of the proposed algorithm are further investigated via a set of experiments. They indicate that such a filter formulation can increase the analysis quality, even for relatively small ensemble sizes, compared to other ensemble filters in nonlinear, non-Gaussian scenarios. Localization enhances the potential applicability of this PF-inspired scheme in larger-dimensional systems. The proposed algorithm, which is fairly easy to implement and computationally efficient, is referred to as the nonlinear ensemble transform filter (NETF).

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Lang, Charles. "An Adaptive Model of Student Performance Using Inverse Bayes." Journal of Learning Analytics 1, no. 3 (November 28, 2014): 154–56. http://dx.doi.org/10.18608/jla.2014.13.10.

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I propose a coherent framework for the use of Inverse Bayesian estimation to summarize and make predictions of student behavior in adaptive educational settings. The Inverse Bayes Filter utilizes Bayes theorem to estimate the relative impact of contextual factors and internal student factors on student performance using time series data across a range of possible dimensions. The Inverse Bayesian algorithm treats the student as a Bayesian learner; her partial credit score or confidence is proportional to her prior knowledge and how she interprets her environment. Once the algorithm has weighted internal and external factors this information is used to make a prediction about the student’s next attempt.

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Wu, Wei Hua, Jing Jiang, Chong Yang Liu, and Xiong Hua Fan. "Fast Gaussian Mixture Probability Hypothesis Density Filter." Applied Mechanics and Materials 568-570 (June 2014): 550–56. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.550.

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Although the Gaussian mixture probability hypothesis density (GMPHD) filter is a multi-target tracker that can alleviate the computational intractability of the optimal multi-target Bayes filter and its computational complex is lower than that of sequential Monte Carlo probability hypothesis density (SMCPHD), its computational burden can be reduced further. In the standard GMPHD filter, each observation should be matched with each component when the PHD is updated. In practice, time cost of evaluating many unlikely measurements-to-components parings is wasteful, because their contribution is very limited. As a result, a substantial reduction in complexity could be obtained by directly setting relative value associated with these parings. A fast GMPHD algorithm is proposed in the paper based on gating strategy. Simulation results show that the fast GMPHD can save computational time by 60%~70% without any degradation in performance compared with standard GMPHD.

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Jang, Hoon-Seok, Mannan Saeed Muhammad, and Tae-Sun Choi. "Bayes Filter based Jitter Noise Removal in Shape Recovery from Image Focus." Journal of Imaging Science and Technology 63, no. 2 (March 1, 2019): 20501–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2019.63.2.020501.

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Darmawan, Matthew Rio, Heru Purnomo Ipung, and Maulahikmah Galinium. "Experiment of Multispectral Sensing Sensor for Urban Road Materials in Outdoor Environment." ICONIET PROCEEDING 2, no. 3 (February 13, 2019): 191–99. http://dx.doi.org/10.33555/iconiet.v2i3.32.

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This research is the first attempt to conduct several experiments of multispectralsensing sensor for urban road materials in outdoor environment. This research aims to classifyfive urban road materials that are aggregates, asphalts, concrete, clay, natural fibre includingvegetation and water. There were 9 cameras in the multispectral sensing sensor. Seven cameraattached with narrow band optical filter with the centre spectrum at 710nm, 730nm, 750nm,800nm, 870nm, 905nm and 950nm. One camera attached with 720 nm normalization band useshigh pass optical filter. Another camera attached with UV/IR cut optical filter works as a RGBcamera. The images results, that have been taken, are processed in MATLAB to get the imagingindex results from the multispectral system. Naïve Bayes classifier is used in Weka to classifythe urban road materials with vegetation and water. The first classification and testing thatclassifies five urban road materials with vegetation and water have accuracy results ranged from0 % to 32% while the accuracy results without vegetation and water have better accuracy resultsranged from 0 % to 55 %.

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Celard, P., A. Seara Vieira, E. L. Iglesias, and L. Borrajo. "LDA filter: A Latent Dirichlet Allocation preprocess method for Weka." PLOS ONE 15, no. 11 (November 9, 2020): e0241701. http://dx.doi.org/10.1371/journal.pone.0241701.

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This work presents an alternative method to represent documents based on LDA (Latent Dirichlet Allocation) and how it affects to classification algorithms, in comparison to common text representation. LDA assumes that each document deals with a set of predefined topics, which are distributions over an entire vocabulary. Our main objective is to use the probability of a document belonging to each topic to implement a new text representation model. This proposed technique is deployed as an extension of the Weka software as a new filter. To demonstrate its performance, the created filter is tested with different classifiers such as a Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Naive Bayes in different documental corpora (OHSUMED, Reuters-21578, 20Newsgroup, Yahoo! Answers, YELP Polarity, and TREC Genomics 2015). Then, it is compared with the Bag of Words (BoW) representation technique. Results suggest that the application of our proposed filter achieves similar accuracy as BoW but greatly improves classification processing times.

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Vrettos, S., and A. Stafylopatis. "A taxonomy fuzzy filtering approach." Journal of Automatic Control 13, no. 1 (2003): 25–29. http://dx.doi.org/10.2298/jac0301026v.

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Our work proposes the use of topic taxonomies as part of a filtering language. Given a taxonomy, a classifier is trained for each one of its topics. 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. Using the trained 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 our study, Support Vector Machines and Na?ve Bayes classifiers were used to provide topic probabilities. Aggregation of topic probabilities based on fuzzy logic operators was found to improve filtering performance on the Renters text corpus, as compared to the use of their Boolean counterparts. Finally, we deployed a filtering system on the web using a sample taxonomy of the Open Directory Project.

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Qiu, Hao, Gaoming Huang, and Jun Gao. "Centralized multi-sensor multi-target tracking with labeled random finite set." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 231, no. 4 (August 6, 2016): 669–76. http://dx.doi.org/10.1177/0954410016641447.

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Tracking multiple objects with multiple sensors is widely recognized to be much more complex than the single-sensor scenario. This contribution proposes a computationally tractable multi-sensor multi-target tracker. Based on Bayes equation and multi-senor observation model, a new corrector for multi-senor is derived. To lower the complexity of update operation, a parallel track-to-measurement association strategy is applied to the corrector. Hypotheses truncation scheme along with first-moment approximation of multi-target density are also employed to improve the tracking efficiency. The tracker is applied to a couple-sensor scenario. Experiment results validate the advantages of proposed method compared to the standard single-sensor δ-generalized labeled multi-Bernoulli filter and the iterated-corrector probability hypothesis density filter.

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Zhang, Yun, Yude Zhang, Wei He, Shujuan Yu, and Shengmei Zhao. "Improved feature size customized fast correlation-based filter for Naive Bayes text classification." Journal of Intelligent & Fuzzy Systems 38, no. 3 (March 4, 2020): 3117–27. http://dx.doi.org/10.3233/jifs-191066.

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Lee, Chang-Hwan. "An information-theoretic filter approach for value weighted classification learning in naive Bayes." Data & Knowledge Engineering 113 (January 2018): 116–28. http://dx.doi.org/10.1016/j.datak.2017.11.002.

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Nakabayashi, Akio, and Genta Ueno. "An Extension of the Ensemble Kalman Filter for Estimating the Observation Error Covariance Matrix Based on the Variational Bayes’s Method." Monthly Weather Review 145, no. 1 (December 21, 2016): 199–213. http://dx.doi.org/10.1175/mwr-d-16-0139.1.

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Abstract This paper presents an extension of the ensemble Kalman filter (EnKF) that can simultaneously estimate the state vector and the observation error covariance matrix by using the variational Bayes’s (VB) method. In numerical experiments, this capability is examined for a time-variant observation error covariance matrix, and it is noteworthy that this method works well even when the true observation error covariance matrix is nondiagonal. In addition, two complementary studies are presented. First, the stability of a long-run assimilation is demonstrated when there are unmodeled disturbances. Second, a maximum-likelihood (ML) method is derived and demonstrated for optimizing the hyperparameters used in this method.

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Virk, Amardeep Singh, Mandeep Kaur, and Lovely Passrija. "Performance Evaluation of Image Enhancement Techniques in Spatial and Wavelet Domains." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 1 (August 1, 2012): 162–66. http://dx.doi.org/10.24297/ijct.v3i1c.2771.

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Denoising is one of the important tasks in image processing. Despite the significant research conducted on this topic, the development of efficient denoising methods is still a compelling challenge. In this paper, spatial domain methods and Wavelet Domain Methods of image denoising have been evaluated. The medical ultrasound images suffer from speckle noise which is multiplicative in nature and more difficult to remove than additive noise. In the spatial filter methods Median Filter and Wiener Filter are implemented. These methods are based on the simple formulas that are proposed by different authors. In Wavelet Methods Visu Shrink, Neigh shrink and Bayes Shrink are implemented. The basic idea of wavelet methods is to denoise the image by applying wavelet transform to the noisy image, then thresholding the detailed wavelet coefficient and inverse transforming the set of thresholded coefficient to obtain the denoised image. The comparison of all filters methods is done using various Quality Metrics like Peak Signal-to-Noise Ratio (PSNR), Bit Error Rate (BER), Mean Square Error, etc. The filters methods implemented in MATLAB 7.10.0.499(R2010a).

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Journal articles on the topic 'Bayesův filtr'

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