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58 results for “Random Field”
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Research ArticleOpen accessPhrase in the titleA Comparison between Conditional Random Field and Structured Support Vector Machine for Arabic Named Entity RecognitionMarwa Muhammad, Muhammad Rohaim, Alaa Hamouda, Salah Abdel-MageidThe Named Entity Recognition (NER) is an integrated task in many NLP applications such as machine translation, Information extraction and question answering. Arabic is one of the authorised spoken languages in the united nation. Currently, there is much Arabic information on the internet, so, nowadays the need for tools which process this information becomes significant. In this study, we have … …Journal of Computer Science, Vol. 16, Issue 1 · 30 Jan 2020 · 8,113 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleNamed Entity Recognition for Kannada using Gazetteers list with Conditional Random FieldsK. P. Pallavi, L. Sobha, M. M. RamyaNamed Entities (NEs) that exist in the sentences are essential to build Natural Language Processing (NLP) applications for Information Extraction (IE) from large corpora. However, generating a large corpus is challenging for resource poor languages, such as Kannada. Further, there is no annotated corpus available online. The challenges faced in annotating NEs with pre-defined classes are: It is morphologically joined … …Journal of Computer Science, Vol. 14, Issue 5 · 24 Feb 2018 · 8,275 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleA Flexible, Real-Time Algorithm for Simulating Correlated Random Fields and Its PropertiesMichael A. Kouritzin, Fraser Newton, Biao WuContemporary real-time problems like CAPTCHA generation and optical character recognition can be solved effectively using correlated random fields. These random fields should be produced on a graph in order that problems of any dimension and shape can be handled. However, traditional solutions are often too slow, inaccurate or both. Herein, the Quick Simulation Random Field algorithm to produce correlated random … …Journal of Mathematics and Statistics, Vol. 13, Issue 3 · 29 Sep 2017 · 7,069 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleInverse Correlations for Multiple Time Series and Gaussian Random Fields and Measures of Their Linear DeterminismR.J. Bhansali, L. IppolitiFor a discrete-time vector linear stationary process, {X(t)}, admitting forward and backward autoregressive representations, the variance matrix of an optimal linear interpolator of X(t), based on a knowledge of {X(t-j), j≠0}, is known to be given by Ri(0)-1 where Ri(0) denotes the inverse variance of the process. Let A=Is-1Ri(0)ˉ1R(0)ˉ1, where R(0) denotes the variance matrix of {X(t)} and Is an … …Journal of Mathematics and Statistics, Vol. 1, Issue 4 · 31 Dec 2005 · 6,424 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleAccessing the Appropriateness of a Spatial Regression Using Generalized (h1h2-)Slepian Random FieldWayan Somayasa, Jamhir Safani, La Ode Ngkoimani, Muhamad Zamrun F.In this study we derived asymptotic goodness-of-fit test (model check) for spatial regression where the critical region as well as the p-value of the tests are approximated based on the distribution of a type of the integral functional of the generalized (h1h2-)-Slepian field and the set-indexed Gaussian white noise. Such random fields are obtained as the limit process of the … …Journal of Mathematics and Statistics, Vol. 13, Issue 2 · 2 May 2017 · 7,362 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleConvergence of Renormalization Group Transformations of Gibbs Random FieldFarida Kachapova, Ilias KachapovStatistical mechanics describes interaction between particles of a physical system. Particle properties of the system can be modelled with a random field on a lattice and studied at different distance scales using renormalization group transformation. Here we consider a thermodynamic limit of a lattice model with weak interaction and we use semi-invariants to prove that random fields transformed by renormalization … …Journal of Mathematics and Statistics, Vol. 12, Issue 3 · 27 Jul 2016 · 7,012 accessesView PDFCite
Research ArticleOpen accessIn the keywordsRange Image Segmentation by Randomized Region Growing and Bayesian Edge RegularizationSmaine Mazouzi, Mohamed BatoucheWe presented and evaluated a new Bayesian method for range image segmentation. The method proceeds in to stages. First, an initial segmentation was produced by a randomized region growing technique. The produced segmentation was considered as a degraded version of the ideal segmentation, which should be then refined. In the second stage, image pixels not labeled in the first stage … …Journal of Computer Science, Vol. 3, Issue 5 · 31 May 2007 · 6,924 accessesView PDFCite
Research ArticleOpen accessIn the keywordsUniform Twister Plane GeneratorAleksei F. Deon, Yulian A. MenyaevRandom plane generators may use various types of the random number algorithms to create multidimensional planes. At the same time, the discrete Descartes random planes have to be uniform. The matter is that using the concept of the uncontrolled random generation may lead to a result of weak quality due to initial sequences having either insufficient uniformity or skipping of … …Journal of Computer Science, Vol. 14, Issue 2 · 26 Feb 2018 · 11,514 accessesView PDFCite
Research ArticleOpen accessIn the keywordsField Programmable Gate Array Based RF-THI Pulse Width Modulation Control for Three Phase Nverter Using Matlab Modelsim CosimulationValantina Stephen, L. Padma Suresh, P. MuthukumarThis study proposes different configurations of PWM techniques for harmonic reduction and improvement of fundamental peak voltage. For providing adjustable frequency power to industrial applications, three-phase inverter is preferred. The general aim of this study is to reduce the third order harmonics and improve the Harmonic Spread Factor by different PWM techniques using matlab modelsim cosimulation. The gating signals to … …American Journal of Applied Sciences, Vol. 9, Issue 11 · 1 Sep 2012 · 7,443 accessesView PDFCite
Research ArticleOpen accessIn the keywordsA New Modified Gaussian Mixture Model for Color-Texture SegmentationM. Sujaritha, S. AnnaduraiProblem statement: This study presents a new, simple and efficient modified Gaussian mixture model based clustering algorithm for color-texture segmentation. The proposed mixture model introduces a new component density function which incorporates spatial information and the weighting factor for neighborhood effect is fully adaptive to the image content. Approach: It enhances the smoothness towards piecewise-homogeneous segmentation and reduces the edge-blurring … …Journal of Computer Science, Vol. 7, Issue 2 · 25 Feb 2011 · 7,329 accessesView PDFCite