1 citations · 3 across the 9 of their papers we have counts for
9 papers
Image Segmentation, Compression and Reconstruction from Edge Distribution Estimation with Random Field and Random Cluster Theories
Robert A. Murphy
Random field and random cluster theory are used to describe certain mathematical results concerning the probability distribution of image pixel intensities characterized as generic…
Illustrating the Suitability of Greedy and Dynamic Algorithms Using The Economics Concept of "Opportunity Cost"
Eugene Callahan, Robert Murphy, Anas Elghafari
Students of Computer Science often wonder when, exactly, one can apply a greedy algorithm to a problem, and when one must use the more complicated and time-consuming techniques of…
Auto-encoding a Knowledge Graph Using a Deep Belief Network: A Random Fields Perspective
Robert A. Murphy
We started with a knowledge graph of connected entities and descriptive properties of those entities, from which, a hierarchical representation of the knowledge graph is derived. U…
A Brownian Motion Model and Extreme Belief Machine for Modeling Sensor Data Measurements
Robert A. Murphy
As the title suggests, we will describe (and justify through the presentation of some of the relevant mathematics) prediction methodologies for sensor measurements. This exposition…
A Critical Connectivity Radius for Segmenting Randomly-Generated, High Dimensional Data Points
Robert A. Murphy
Motivated by a -dimensional (unsupervised) image segmentation task whereby local regions of pixels are clustered via edge detection methods, a more general probabilistic mathema…
A Predictive Model using the Markov Property
Robert A. Murphy
Given a data set of numerical values which are sampled from some unknown probability distribution, we will show how to check if the data set exhibits the Markov property and we wil…
Estimating the Mean Number of K-Means Clusters to Form
Robert A. Murphy
Utilizing the sample size of a dataset, the random cluster model is employed in order to derive an estimate of the mean number of K-Means clusters to form during classification of…
A Neural Network Anomaly Detector Using the Random Cluster Model
Robert A. Murphy
The random cluster model is used to define an upper bound on a distance measure as a function of the number of data points to be classified and the expected value of the number of…
On The Sharp Threshold Interval Length of Partially Connected Random Geometric Graphs During K-Means Classification
Robert A. Murphy
In -means classification, a set of data will form clusters, i.e. classes, if the measured distances between data points (or some common point in each class) are below a certain…