Towards Expectation-Maximization by SQL in RDBMS

Expectation Maximization

Expectation Maximization

The Expectation Maximization (EM) algorithm [1, 2] is one of the most widely used algorithms in statistics. Suppose we are given some observed data X and a model family parametrized by θ, and would like to find the θ which maximizes p(X |θ), i.e. the maximum likelihood estimator. The basic idea of EM is actually quite simple: when direct maximization of p(X |θ) is complicated we can augment the...

Towards a Malay Derivational Lexicon: Learning Affixes Using Expectation Maximization

We propose an unsupervised training method to guide the learning of Malay derivational morphology from a set of morphological segmentations produced by a naı̈ve morphological analyzer. Using a morphology-based language model, we first estimate the probability of a given segmentation. We train the model with EM to find the segmentation that maximizes the probability of each morpheme. We extract t...

Bilateral Filtering with Clusters by Expectation Maximization

Image restoration keeping sharp edges is achieved by bilateral filter. In this paper, an approach to improve edges for the filter is presented. The proposed algorithm relies on clustering by Expectation Maximization that produced clusters of intensive values. A stage is followed where standard deviation of Gaussian filters for scales of the spatial and intensity are adjusted by features of the ...

Spectrometer Calibration by Expectation Maximization Method

Expectation Maximization (EM) algorithm is a parameter estimation method from incomplete observations. In this paper, an implementation of this method to the calibration of HKS spectrometer at Jefferson Lab is described. We show that the application of EM method is able to calibrate the spectrometer properly in the presence of high background noise, while the traditional nonlinear Least Square ...