The Expectation-Maximization (EM) algorithm is a hill-climbing approach to finding a local maximum of a likelihood function [7, 8]. The EM algorithm alternates between finding a greatest lower bound to the likelihood function (the “E Step”), and then maximizing this bound (the “M Step”). The EM algorithm belongs to a broader class of alternating minimization algorithms [6], which includes the A...

This paper proposes an algorithm which can write programs automatically to solve problems. We model the sequence of instructions as a n-gram language model and the sequence is represented by some hidden variables. Expectation maximization algorithm is applied to train the n-gram model and perform program induction. Our approach is very flexible and can be applied to many problems. In this paper...

Domain adaptation (DA) is the task of classifying an unlabeled dataset (target) using a labeled dataset (source) from a related domain. The majority of successful DA methods try to directly match the distributions of the source and target data by transforming the feature space. Despite their success, state of the art methods based on this approach are either involved or unable to directly scale...

BACKGROUND Post-therapy SPECT/CT imaging of 90Y microspheres delivered to hepatic malignancies is difficult, owing to the continuous, high-energy Bremsstrahlung spectrum emitted by 90Y. This study aimed to evaluate the utility of a commercially available software package (HybridRecon, Hermes Medical Solutions AB) which incorporates full Monte Carlo collimator modelling. Analysis of image qualit...

In probabilistic modeling, it is often useful to change the structure, or refactor, a model, so that it has a different number of components, different parameter sharing, or other constraints. For example, we may wish to find a Gaussian mixture model (GMM) with fewer components that best approximates a reference model. Maximizing the likelihood of the refactored model under the reference model ...

We present the Trusted Expectation-Maximization (TEM), a new discriminative training scheme, for speech recognition applications. In particular, the TEM algorithm may be used for Hidden Markov Models (HMMs) based discriminative training. The TEM algorithm has a form similar to the ExpectationMaximization (EM) algorithm, which is an efficient iterative procedure to perform maximum likelihood in ...

Images and data files provide an excellent opportunity for concealing illegal or clandestine material. Currently, there are over 250 different tools which embed data into an image without causing noticeable changes to the image. From a forensics perspective, when a system is confiscated or an image of a system is generated the investigator needs a tool that can scan and accurately identify file...

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 ...

We use the Expectation-Maximization (EM) algorithm to classify 3D aerial lidar scattered height data into four categories: road, grass, buildings, and trees. To do so we use five features: height, height variation, normal variation, lidar return intensity, and image intensity. We also use only lidar-derived features to organize the data into three classes (the road and grass classes are merged)...