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Showing 1 to 7 of 7 for “"relevance vector machine"”.

  1. Topics in imbalanced data classification : AdaBoost and Bayesian relevance vector machine

    Wang, WenyangUniversity of Missouri--Columbia2020

    … are presented. The second part treats the Relevance Vector Machine (RVM), which is a supervised learning algorithm extended from the Support Vector Machine (SVM) based on the Bayesian sparsity model. Compared with the regression problem, RVM classification is challenging to conduct because …

    missouri Repository record

  2. Application of the Relevance Vector Machine to Canal Flow Prediction in the Sevier River Basin

    Flake, John T.

    utahstate Repository record

  3. New approaches to open problems in gene expression microarray data

    Marconi, Daniela <1979>Alma Mater Studiorum - Università di Bologna2008

    … evaluation in a three-class prblem by means of Relevance Vector Machine [4] is described. In fact, looking at microarray data in a prognostic and diagnostic clinical framework, not only differences could have a crucial role. In some cases similarities can give useful and, sometimes even more, …

    bologna Repository record

  4. ANALYSIS AND MODELING OF NONSTATIONARY GROUND MOTION COHERENCY

    Abbas, Habeeb F2020

    … are performed using wavelet analysis and relevance vector machine regression. To perform the analysis, earthquake ground motion data from four events recorded at dense seismograph SMART-1 array in north-south and east-west horizontal directions are used to investigate the lagged coherency …

    siu-theses Repository record

  5. Improving Emergency Department Patient Flow Through Near Real-Time Analytics

    Qiu, Shanshan2014

    … for streamlining ED patient flow that employs machine learning, statistical and operations research methods to facilitate its operationalization. </p> <p>ED crowding has become the subject of significant public and academic attention, and it is known to cause a number of adverse outcomes to the …

    wayne-thes Repository record

  6. Machine Learning and Bayesian Statistics for Seismic Compressive Sensing

    Pilikos, GeorgiosUniversity of Cambridge2019

    … use algorithms from the Bayesian statistics and machine learning field that allow the construction of models using probability distributions over random variables. This allows the modelling of sparsity and provides flexibility by adding or removing basis functions from the model. It also provides …

    cambridge Repository record

  7. IMAGE-BASED MODELING AND PREDICTION OF NON-STATIONARY GROUND MOTIONS

    DAK HAZIRBABA, YILDIZ2016

    … of non-stationary ground motions. Using Relevance Vector Machines, a regression model which takes as input a set of seismic predictors, and produces as output the expected evolutionary power spectral density, conditioned on the predictors. A demonstrative example is presented, where …

    siu-theses Repository record